eBay:iPads:: sold classification:: dupobs
bdanalytics
Date: (Sat) Jul 25, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "dupobs_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "sold"
# for classification, the response variable has to be a factor
glb_rsp_var <- "sold.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
# return(log(raw))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))## [1] Y Y N N <NA>
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
# return(exp(var))
as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))## [1] 1 1 0 0 NAif ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
glb_id_var <- c("UniqueID")
glb_category_var <- c("prdline.my")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_derive_lst[["idseq.my"]] <- list(
mapfn=function(UniqueID) { return(UniqueID - 10000) }
, args=c("UniqueID"))
glb_derive_lst[["prdline.my"]] <- list(
mapfn=function(productline) { return(productline) }
, args=c("productline"))
glb_derive_lst[["startprice.log"]] <- list(
mapfn=function(startprice) { return(log(startprice)) }
, args=c("startprice"))
# glb_derive_lst[["startprice.log.zval"]] <- list(
glb_derive_lst[["descr.my"]] <- list(
mapfn=function(description) { mod_raw <- description;
# Modifications for this exercise only
# Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
mod_raw <- gsub("(\\w)(\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 10\\.SCREEN ", " 10\\. SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" ans ", " and ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("Best Buy", "BestBuy", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" conditon|condtion|conditions", " condition", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(CONDITION|ONLY)\\.(\\w)", "\\1\\. \\2", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" cracksNo ", " cracks No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" drop ", " dropped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fineiCloud ", " fine iCloud ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("iCL0UD", "iCL0UD", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^READiPad ", "READ iPad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" REFURB\\.", " REFURBISHED\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" SCREEB ", " SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^somescratches ", "some scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("SWAPPA\\.COM", "SWAPPACOM", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" touchscreen ", " touch screen ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
return(mod_raw) }
, args=c("description"))
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- c("descr.my")
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
glb_append_stop_words[["descr.my"]] <- c(NULL
# freq = 1
,"511","825","975"
,"a1314","a1430","a1432"
,"abused","across","adaptor","add","antenna","area","arizona"
,"beginning","bidder","bound"
,"changed","chrome","consumer","contents","control","cream"
,"decent","defender","depicted","disclaimer","distressed","divider"
,"dlxnqat9g5wt","done","dont","dust","duty"
,"erased","ereader","exact"
,"film","flickers"
,"generic","genuine","glitter"
,"hdmi","higher","hospital"
,"impact","instead","interior"
,"jack","july"
,"keeps"
,"late","letters","lifting","limited","lining","liquid"
,"local","long","longer","looping","loss"
,"mb292ll","mc707ll","mc916ll","mc991ll","md789ll","mf432ll","mgye2ll"
,"mind","mixed"
,"neither","none","november"
,"occasional","outside"
,"period","pet","played","plug","poor","portion","pouch","price","provided"
,"ranging"
,"recently","red","reflected","repeat","required","reserve","residue","result"
,"roughly","running"
,"said","seconds","seem","semi","send","serious","setup"
,"shell","short","size","slice","smoke"
,"softer","software","somewhat","soon"
,"sparingly","sparkiling","special","speed"
,"stains","standup","status","stopped","subtle","sustained","swappacom"
,"technical","texture","therefore","though","totally","touchy"
,"university","unknown","untouched","upgrade"
,"valid","vary","version"
,"want","website","winning","wrapped"
,"zaag","zombie"
)
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_filter_txt_terms <- "top" # or "sparse"
glb_top_n <- c(10)
names(glb_top_n) <- glb_txt_vars
glb_sprs_thresholds <- c(0.950) # Generates 10 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("productline", "startprice", "description")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "glmnet", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "glmnet", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL #"Low.cor.X.glm"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 9.156 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here")
glb_drop_obs <- c(
11234, #sold=0; 2 other dups(10306, 11503) are sold=1
11844, #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
NULL)
glb_allobs_df <- glb_allobs_df[!glb_allobs_df[, glb_id_var] %in% glb_drop_obs, ]
# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 304 duplicates by all features:"myprint_df(dup_allobs_df)## description biddable startprice condition cellular
## 1711 1 0.99 For parts or not working Unknown
## 2608 1 0.99 For parts or not working Unknown
## 293 1 5.00 Used Unknown
## 478 1 5.00 Used Unknown
## 385 0 15.00 Used 0
## 390 0 15.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1711 Unknown Unknown 16 Unknown 1 11711 Train
## 2608 Unknown Unknown 16 Unknown NA 12608 Test
## 293 Unknown White 16 Unknown 1 10293 Train
## 478 Unknown White 16 Unknown 1 10478 Train
## 385 None Black 16 Unknown 0 10385 Train
## 390 None Black 16 Unknown 0 10390 Train
## description biddable startprice condition cellular
## 1956 1 0.99 Used 0
## 828 1 249.97 Manufacturer refurbished 1
## 3 0 199.99 Used 0
## 1649 0 209.00 For parts or not working Unknown
## 2111 1 200.00 Used 0
## 172 0 269.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1956 None Unknown 16 iPad 2 NA 11956 Test
## 828 Unknown Black 64 iPad 2 0 10828 Train
## 3 None White 16 iPad 4 1 10003 Train
## 1649 Unknown Unknown 16 iPad Air 0 11649 Train
## 2111 None Space Gray 64 iPad mini 2 NA 12111 Test
## 172 None Unknown 32 iPad mini 2 0 10172 Train
## description biddable startprice condition cellular carrier color
## 8 0 329.99 New 0 None White
## 660 0 329.99 New 0 None White
## 319 0 345.00 New 0 None Gold
## 1886 0 345.00 New 0 None Gold
## 1363 0 498.88 New 1 Verizon Gold
## 1394 0 498.88 New 1 Verizon Gold
## storage productline sold UniqueID .src
## 8 16 iPad mini 3 0 10008 Train
## 660 16 iPad mini 3 0 10660 Train
## 319 16 iPad mini 3 1 10319 Train
## 1886 16 iPad mini 3 NA 11886 Test
## 1363 16 iPad mini 3 0 11363 Train
## 1394 16 iPad mini 3 0 11394 Train# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 40 40 0 6 3 9
## 106 106 0 4 1 5
## 9 9 0 1 3 4
## 17 17 0 3 1 4
## 36 36 0 3 1 4
## 53 53 0 2 2 4
## .grpid sold.0 sold.1 sold.NA .freq
## 10 10 0 2 0 2
## 42 42 0 1 1 2
## 57 57 1 0 1 2
## 66 66 1 0 1 2
## 91 91 0 1 1 2
## 101 101 0 1 1 2
## .grpid sold.0 sold.1 sold.NA .freq
## 130 130 1 0 1 2
## 131 131 1 1 0 2
## 132 132 0 1 1 2
## 133 133 2 0 0 2
## 134 134 0 1 1 2
## 135 135 2 0 0 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 4 4 1 1 0 2
## 22 22 1 1 0 2
## 23 23 1 1 0 2
## 74 74 1 1 0 2
## 83 83 1 1 0 2
## 84 84 1 1 0 2
## 95 95 1 1 0 2
## 102 102 1 1 0 2
## 109 109 1 1 0 2
## 111 111 1 1 0 2
## 122 122 1 1 0 2
## 131 131 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 1 1 0 1 1 2
## 5 5 1 0 1 2
## 7 7 0 0 2 2
## 8 8 1 0 1 2
## 9 9 0 1 3 4
## 12 12 0 0 2 2
## 14 14 0 1 1 2
## 15 15 0 0 2 2
## 17 17 0 3 1 4
## 18 18 0 2 1 3
## 19 19 0 2 1 3
## 24 24 0 2 1 3
## 26 26 1 0 1 2
## 28 28 1 0 1 2
## 30 30 0 1 1 2
## 32 32 0 0 2 2
## 33 33 0 1 1 2
## 35 35 0 2 1 3
## 36 36 0 3 1 4
## 37 37 0 0 2 2
## 38 38 0 1 1 2
## 40 40 0 6 3 9
## 41 41 0 0 2 2
## 42 42 0 1 1 2
## 43 43 0 1 1 2
## 44 44 0 2 1 3
## 47 47 0 1 1 2
## 48 48 0 0 2 2
## 49 49 0 1 2 3
## 51 51 0 1 1 2
## 53 53 0 2 2 4
## 54 54 0 1 1 2
## 55 55 1 0 2 3
## 56 56 1 0 1 2
## 57 57 1 0 1 2
## 58 58 0 0 2 2
## 59 59 1 0 1 2
## 60 60 1 0 1 2
## 63 63 0 1 1 2
## 66 66 1 0 1 2
## 67 67 1 0 1 2
## 68 68 0 0 2 2
## 69 69 1 0 1 2
## 73 73 0 1 1 2
## 76 76 0 2 1 3
## 86 86 0 0 2 2
## 87 87 1 0 1 2
## 89 89 1 0 1 2
## 90 90 0 0 2 2
## 91 91 0 1 1 2
## 93 93 0 1 1 2
## 94 94 1 0 1 2
## 99 99 0 1 1 2
## 101 101 0 1 1 2
## 103 103 0 1 1 2
## 104 104 1 0 1 2
## 106 106 0 4 1 5
## 107 107 0 1 1 2
## 108 108 0 1 1 2
## 112 112 1 0 1 2
## 114 114 0 1 1 2
## 115 115 0 1 1 2
## 116 116 1 0 1 2
## 117 117 0 2 1 3
## 118 118 0 1 1 2
## 121 121 1 0 1 2
## 124 124 1 0 1 2
## 128 128 0 1 1 2
## 130 130 1 0 1 2
## 132 132 0 1 1 2
## 134 134 0 1 1 2glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
glb_exclude_vars_as_features <- c(".grpid", glb_exclude_vars_as_features)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 9.156 14.873 5.717
## 2 inspect.data 2 0 14.873 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## Loading required package: reshape2
## sold.0 sold.1 sold.NA
## Test NA NA 798
## Train 999 860 NA
## sold.0 sold.1 sold.NA
## Test NA NA 1
## Train 0.5373857 0.4626143 NA
## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## sold sold.fctr .n
## 1 0 N 999
## 2 1 Y 860
## 3 NA <NA> 798## Warning: Removed 1 rows containing missing values (position_stack).
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 798
## Train 999 860 NA
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 1
## Train 0.5373857 0.4626143 NA# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 14.873 18.948 4.075
## 3 scrub.data 2 1 18.948 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
sel_obs <- function(vars_lst, ignore.case=TRUE, perl=FALSE) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
# if (!is.null(Popular)) {
# if (is.na(Popular))
# tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
# tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
# }
# if (!is.null(NewsDesk))
# tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
for (var in names(vars_lst)) {
if (grepl(".contains", var))
tmp_df <- tmp_df[grep(vars_lst[var],
tmp_df[, unlist(strsplit(var, ".contains"))],
ignore.case=ignore.case, perl=perl), ]
else
tmp_df <- tmp_df[tmp_df[, var] == vars_lst[var], ]
}
return(glb_allobs_df[, glb_id_var] %in% tmp_df[, glb_id_var])
}
#print(glb_allobs_df[sel_obs(list(description.contains="mini(?!m)"), perl=TRUE), "description"])
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cols),
FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE)
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1593 0 0 0 0 0
## 1 288 0 4 36 27 172 196
## Unknown 4 4 2 0 0 331 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1597 0 0 0 0 0
## 1 292 0 6 36 27 172 196
## Unknown 0 0 0 0 0 331 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 18.948 19.654 0.706
## 4 transform.data 2 2 19.655 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: idseq.my..."
## [1] "Creating new feature: prdline.my..."
## [1] "Creating new feature: startprice.log..."
## [1] "Creating new feature: descr.my..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10178, 10948, 11514, 11904, 12157, 12210)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
glb_allobs_df[row_pos, "descr.my"] <-
gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 19.655 20.128 0.473
## 5 extract.features 3 0 20.128 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 20.135 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 20.135 20.154
## 2 extract.features_factorize.str.vars 2 0 20.154 NA
## elapsed
## 1 0.019
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid prdline.my
## "storage" "productline" ".src" ".grpid" "prdline.my"
## descr.my
## "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5## Warning: Creating factors of string variable: prdline.my: # of unique
## values: 12if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE,
preserve_intra_word_dashes=TRUE, lazy=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_DTM_terms <- function(DTM) {
TfIdf_mtrx <- as.matrix(DTM)
rownames(TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
TfIdf_vctr <- colSums(TfIdf_mtrx)
names(TfIdf_vctr) <- dimnames(DTM)[[2]]
TfIdf_df <- as.data.frame(TfIdf_vctr)
names(TfIdf_df) <- "TfIdf"
TfIdf_df$term <- rownames(TfIdf_df)
TfIdf_df$freq <- colSums(TfIdf_mtrx != 0)
TfIdf_df$pos <- 1:nrow(TfIdf_df)
return(TfIdf_df <- orderBy(~ -TfIdf, TfIdf_df))
}
get_corpus_terms <- function(txt_corpus) {
TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
return(TfIdf_df <- get_DTM_terms(TfIdf_DTM))
}
#stop(here")
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
#tolower Not needed as of version 0.6.2 ?
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=FALSE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=FALSE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")), lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_TfIdf_df <- inspect_terms(txt_corpus)
#stopped_words_TfIdf_df[grepl("cond", stopped_words_TfIdf_df$term, ignore.case=TRUE), ]
#txt_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#which(txt_X_mtrx[, 211] > 0)
#glb_allobs_df[which(txt_X_mtrx[, 211] > 0), glb_txt_vars]
#txt_X_mtrx[2159, txt_X_mtrx[2159, ] > 0]
# txt_corpus <- tm_map(txt_corpus, stemDocument, "english", lazy=TRUE) #Done below
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_TfIdf_df <- inspect_terms(txt_corpus)
#stemmed_words_TfIdf_df[grepl("cond", stemmed_words_TfIdf_df$term, ignore.case=TRUE), ]
#stm_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#glb_allobs_df[which((stm_X_mtrx[, 180] > 0) | (stm_X_mtrx[, 181] > 0)), glb_txt_vars]
#glb_allobs_df[which((stm_X_mtrx[, 181] > 0)), glb_txt_vars]
# glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
#stop(here")
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_TfIdf_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_TfIdf_mtrx_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf(" Top_n stop TfIDf terms for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.n.post.stop <- rowSums(TfIdf_stop_mtrx > 0)
tmp_allobs_df$TfIdf.sum.post.stop <- rowSums(TfIdf_stop_mtrx)
print(sprintf(" Top_n stem TfIDf terms for %s:", txt_var))
glb_corpus_lst[[txt_var]] <- tm_map(glb_corpus_lst[[txt_var]], stemDocument,
"english", lazy=TRUE) #Features ???
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stem_mtrx
tmp_allobs_df$terms.n.post.stem <- rowSums(TfIdf_stem_mtrx > 0)
tmp_allobs_df$TfIdf.sum.post.stem <- rowSums(TfIdf_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.n.post.stem / tmp_allobs_df$terms.n.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio),
"terms.n.stem.stop.Ratio"] <- 1.0
tmp_allobs_df$TfIdf.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$TfIdf.sum.post.stem / tmp_allobs_df$TfIdf.sum.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$TfIdf.sum.stem.stop.Ratio),
"TfIdf.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df),
sep="")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_df <- get_DTM_terms(full_TfIdf_DTM)
full_TfIdf_df <- full_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(full_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".full", sep="")
names(full_TfIdf_df) <- col_names
# full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
# rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
# full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
# names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
# full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
# names(full_TfIdf_df) <- "TfIdf.full"
# full_TfIdf_df$term <- rownames(full_TfIdf_df)
# full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
# full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_df <- get_DTM_terms(sprs_TfIdf_DTM)
sprs_TfIdf_df <- sprs_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(sprs_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".sprs", sep="")
names(sprs_TfIdf_df) <- col_names
# sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
# names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
# sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
# names(sprs_TfIdf_df) <- "TfIdf.sprs"
# sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
# sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
# sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_DTM_terms(glb_full_DTM_lst[[txt_var]])
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
if (glb_filter_txt_terms == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glb_filter_txt_terms == "top") {
txt_X_df <- txt_full_X_df[, terms_full_df$pos[1:glb_top_n[[txt_var]]], FALSE]
} else stop("glb_filter_txt_terms should be one of c('sparse', 'top') vs. '",
glb_filter_txt_terms, "'")
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 20.154 20.519
## 3 extract.features_process.text 3 0 20.520 NA
## elapsed
## 2 0.365
## 3 NA
## [1] "Building glb_txt_lst..."
## [1] "running gsub for 10 (of 178): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 178): #\\bAlejandro G\\. Iñárritu#..."
## [1] "running gsub for 30 (of 178): #\\bC\\.A\\.A\\.#..."
## [1] "running gsub for 40 (of 178): #\\bCV\\.#..."
## [1] "running gsub for 50 (of 178): #\\bE\\.P\\.A\\.#..."
## [1] "running gsub for 60 (of 178): #\\bG\\.I\\. Joe#..."
## [1] "running gsub for 70 (of 178): #\\bISIS\\.#..."
## [1] "running gsub for 80 (of 178): #\\bJ\\.K\\. Simmons#..."
## [1] "running gsub for 90 (of 178): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 178): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 178): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 178): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 178): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 178): #\\bWall Street#..."
## [1] "running gsub for 150 (of 178): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 178): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 178): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"
## pattern .n
## 1 OK 6
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN DEVICE: Problem with Apple ID"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN SCREEN"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in descr.my:"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## pattern .n
## 1 CONDITION. 8
## 2 ONLY. 6
## 3 GB. 4
## 4 BOX. 2
## 5 CORNER. 2
## 6 ESN. 2
## 7 GOOD. 2
## 8 ICLOUD. 2
## 9 IPADS. 2
## 10 LOCKED. 2
## 11 LOCKS. 2
## 12 ONLY. 2
## 13 SCRATCHES. 2
## 14 TEARS. 2
## 15 USE. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
## pattern .n
## 2 New Open 3
## 4 New Condition 2
## 7 New Digitizer 1
## 8 New Opened 1
## 9 New Scratch 1
## 10 New Screen 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in descr.my: "
## pattern .n
## 1 C Stock 3
## 2 W blue 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in descr.my: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed
## 3 0 20.520 22.231 1.711
## 4 1 22.231 NA NA
## [1] "Remaining compound terms in descr.my: "
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor bgn end elapsed
## 4 1 22.231 22.236 0.005
## 5 0 22.237 NA NA
## [1] "Building glb_corpus_lst..."
## [1] " Top_n stop TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 782; Cols: 4"
## TfIdf term freq pos
## condition 207.6333 condition 497 160
## new 125.3927 new 156 472
## used 122.9018 used 239 747
## good 120.6855 good 197 316
## scratches 113.6830 scratches 254 612
## screen 105.9501 screen 210 614
## TfIdf term freq pos
## read 28.187366 read 31 573
## installed 6.181673 installed 7 362
## outer 5.432193 outer 5 498
## otterbox 5.260069 otterbox 5 497
## else 2.991578 else 3 243
## connect 1.625083 connect 1 162
## TfIdf term freq pos
## high 1.1375583 high 1 341
## hole 1.1375583 hole 1 343
## lights 1.1375583 lights 1 405
## person 1.1375583 person 1 518
## places 1.1375583 places 1 534
## 79in 0.9479652 79in 1 16
## TfIdf term freq pos
## high 1.1375583 high 1 341
## hole 1.1375583 hole 1 343
## lights 1.1375583 lights 1 405
## person 1.1375583 person 1 518
## places 1.1375583 places 1 534
## 79in 0.9479652 79in 1 16## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] " Top_n stem TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 641; Cols: 4"
## TfIdf term freq pos
## condit 207.6181 condit 495 133
## use 145.9567 use 290 611
## scratch 127.9087 scratch 286 499
## new 125.3927 new 156 385
## good 120.7748 good 197 260
## screen 106.8143 screen 213 500
## TfIdf term freq pos
## warranti 16.585575 warranti 24 621
## leather 2.848180 leather 3 324
## card 2.485311 card 3 109
## offer 2.305685 offer 2 395
## known 1.895930 known 1 318
## ope 1.263954 ope 1 400
## TfIdf term freq pos
## final 1.1375583 final 1 228
## fold 1.1375583 fold 1 236
## goe 1.1375583 goe 1 258
## high 1.1375583 high 1 280
## hole 1.1375583 hole 1 282
## 79in 0.9479652 79in 1 16
## TfIdf term freq pos
## final 1.1375583 final 1 228
## fold 1.1375583 fold 1 236
## goe 1.1375583 goe 1 258
## high 1.1375583 high 1 280
## hole 1.1375583 hole 1 282
## 79in 0.9479652 79in 1 16## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.n.post.stop -0.083226560
## TfIdf.sum.post.stop -0.032554183
## terms.n.post.stem -0.083066302
## TfIdf.sum.post.stem -0.035164455
## terms.n.stem.stop.Ratio 0.016573174
## TfIdf.sum.stem.stop.Ratio -0.007890892
## label step_major step_minor bgn end
## 5 extract.features_build.corpus 4 0 22.237 32.907
## 6 extract.features_extract.DTM 5 0 32.908 NA
## elapsed
## 5 10.671
## 6 NA
## [1] "Extracting TfIDf terms for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) charact## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 32.908 34.179 1.271
## 7 extract.features_report.DTM 6 0 34.180 NA NA
## [1] "Reporting TfIDf terms for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 641)>>
## Non-/sparse entries: 8314/1694823
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 8)>>
## Non-/sparse entries: 2067/19189
## Sparsity : 90%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).
## Warning in rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df,
## terms_TfIdf_df): object 'full_TfIdf_mtrx' not found## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 34.180 36.272 2.092
## 8 extract.features_bind.DTM 7 0 36.272 NA NA
## [1] "Binding DTM for descr.my..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 36.272 36.94 0.668
## 9 extract.features_bind.DXM 8 0 36.940 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# dsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# dsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# dsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad mini"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini", glb_rsp_var)))## prdline.my productline D.P.mini sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad 2 iPad 2 0 N 139
## 4 iPad mini iPad mini 0 N 138
## 5 iPad mini iPad mini 0 Y 126
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 121
## 8 iPad mini iPad mini 0 <NA> 108
## 9 iPad Air iPad Air 0 N 102
## 10 iPad 1 iPad 1 0 N 100
## 11 iPad Air 2 iPad Air 2 0 N 100
## 12 iPad 4 iPad 4 0 N 93
## 13 Unknown Unknown 0 <NA> 89
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 81
## 16 iPad 3 iPad 3 0 Y 80
## 17 iPad Air iPad Air 0 Y 78
## 18 iPad Air iPad Air 0 <NA> 74
## 19 iPad 3 iPad 3 0 N 73
## 20 iPad Air 2 iPad Air 2 0 Y 71
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad Air 2 iPad Air 2 0 <NA> 62
## 24 iPad mini 3 iPad mini 3 0 N 61
## 25 iPad mini 2 iPad mini 2 0 N 56
## 26 iPad 3 iPad 3 0 <NA> 55
## 27 iPad mini 2 iPad mini 2 0 <NA> 52
## 28 iPad mini 2 iPad mini 2 0 Y 48
## 29 iPad mini 3 iPad mini 3 0 <NA> 35
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad mini iPad mini 1 N 7
## 32 iPad mini iPad mini 1 Y 5
## 33 iPad mini 2 iPad mini 2 1 <NA> 4
## 34 iPad mini Retina iPad mini Retina 0 Y 4
## 35 iPad mini Unknown 1 <NA> 3
## 36 iPad mini iPad mini 1 <NA> 3
## 37 iPad mini 3 iPad mini 3 1 <NA> 3
## 38 iPad mini Retina iPad mini Retina 0 N 3
## 39 iPad mini 2 iPad mini 2 1 N 2
## 40 iPad mini 3 iPad mini 3 1 N 2
## 41 iPad 5 iPad 5 0 Y 1
## 42 iPad mini Unknown 1 N 1
## 43 iPad mini Unknown 1 Y 1
## 44 iPad mini iPad mini 2 Y 1
## 45 iPad mini 2 iPad mini 2 1 Y 1
## 46 iPad mini Retina iPad mini Retina 1 N 1glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad Air"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air", glb_rsp_var)))## prdline.my productline D.P.air sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad mini iPad mini 0 N 145
## 4 iPad 2 iPad 2 0 N 139
## 5 iPad mini iPad mini 0 Y 132
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 120
## 8 iPad mini iPad mini 0 <NA> 111
## 9 iPad 1 iPad 1 0 N 100
## 10 iPad Air iPad Air 0 N 98
## 11 iPad Air 2 iPad Air 2 0 N 97
## 12 iPad 4 iPad 4 0 N 92
## 13 Unknown Unknown 0 <NA> 88
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 80
## 16 iPad 3 iPad 3 0 Y 79
## 17 iPad Air iPad Air 0 Y 75
## 18 iPad 3 iPad 3 0 N 73
## 19 iPad Air iPad Air 0 <NA> 73
## 20 iPad Air 2 iPad Air 2 0 Y 69
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad mini 3 iPad mini 3 0 N 63
## 24 iPad Air 2 iPad Air 2 0 <NA> 60
## 25 iPad mini 2 iPad mini 2 0 N 58
## 26 iPad mini 2 iPad mini 2 0 <NA> 56
## 27 iPad 3 iPad 3 0 <NA> 55
## 28 iPad mini 2 iPad mini 2 0 Y 49
## 29 iPad mini 3 iPad mini 3 0 <NA> 38
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad Air iPad Air 1 N 4
## 32 iPad mini Retina iPad mini Retina 0 N 4
## 33 iPad mini Retina iPad mini Retina 0 Y 4
## 34 iPad Air iPad Air 1 Y 3
## 35 iPad mini Unknown 0 <NA> 3
## 36 iPad Air 2 iPad Air 2 1 <NA> 2
## 37 iPad Air 2 iPad Air 2 1 N 2
## 38 iPad Air 2 iPad Air 2 1 Y 2
## 39 iPad 3 iPad 3 1 Y 1
## 40 iPad 4 iPad 4 1 N 1
## 41 iPad 5 iPad 5 0 Y 1
## 42 iPad Air Unknown 1 <NA> 1
## 43 iPad Air Unknown 1 N 1
## 44 iPad Air Unknown 1 Y 1
## 45 iPad Air iPad Air 1 <NA> 1
## 46 iPad Air 2 iPad Air 2 2 N 1
## 47 iPad mini Unknown 0 N 1
## 48 iPad mini Unknown 0 Y 1glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# dsp_obs(list(prdline.my="Unknown"), all=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
names(tmp_allobs_df) <- "old.prdline.my"
glb_allobs_df$prdline.my <-
plyr::revalue(glb_allobs_df$prdline.my, c(
# "iPad 1" = "iPad",
# "iPad 2" = "iPad2+",
"iPad 3" = "iPad 3+",
"iPad 4" = "iPad 3+",
"iPad 5" = "iPad 3+",
"iPad Air" = "iPadAir",
"iPad Air 2" = "iPadAir",
"iPad mini" = "iPadmini",
"iPad mini 2" = "iPadmini 2+",
"iPad mini 3" = "iPadmini 2+",
"iPad mini Retina" = "iPadmini 2+"
))
tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))## prdline.my old.prdline.my .n
## 1 iPad 2 iPad 2 442
## 2 iPadmini iPad mini 393
## 3 iPad 1 iPad 1 314
## 4 Unknown Unknown 285
## 5 iPadAir iPad Air 257
## 6 iPadAir iPad Air 2 233
## 7 iPad 3+ iPad 4 225
## 8 iPad 3+ iPad 3 208
## 9 iPadmini 2+ iPad mini 2 163
## 10 iPadmini 2+ iPad mini 3 128
## 11 iPadmini 2+ iPad mini Retina 8
## 12 iPad 3+ iPad 5 1print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))## prdline.my .n
## 1 iPadAir 490
## 2 iPad 2 442
## 3 iPad 3+ 434
## 4 iPadmini 393
## 5 iPad 1 314
## 6 iPadmini 2+ 299
## 7 Unknown 285glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 36.940 75.637 38.697
## 10 extract.features_end 9 0 75.637 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 8 extract.features_bind.DTM 7
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 36.940 75.637 38.697 38.697
## 5 0 22.237 32.907 10.671 10.670
## 7 0 34.180 36.272 2.092 2.092
## 3 0 20.520 22.231 1.711 1.711
## 6 0 32.908 34.179 1.271 1.271
## 8 0 36.272 36.940 0.668 0.668
## 2 0 20.154 20.519 0.365 0.365
## 1 0 20.135 20.154 0.019 0.019
## 4 1 22.231 22.236 0.005 0.005
## [1] "Total Elapsed Time: 75.637 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 20.128 76.969 56.841
## 6 cluster.data 4 0 76.969 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 76.969 77.94 0.971
## 7 manage.missing.data 4 1 77.941 NA NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.TfIdf.sum.post.stop
## 1597 1521 1521
## D.terms.n.post.stem D.TfIdf.sum.post.stem D.T.condit
## 1521 1521 2162
## D.T.use D.T.scratch D.T.new
## 2367 2371 2501
## D.T.good D.T.screen D.T.ipad
## 2460 2444 2425
## D.T.great D.T.work D.T.excel
## 2532 2459 2558
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 1520 1521 1521
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log
## 1521 1520 1522
## D.ndgts.log D.npnct01.log D.npnct02.log
## 2426 2579 2657
## D.npnct03.log D.npnct04.log D.npnct05.log
## 2614 2657 2592
## D.npnct06.log D.npnct07.log D.npnct08.log
## 2554 2656 2581
## D.npnct09.log D.npnct10.log D.npnct11.log
## 2641 2648 2301
## D.npnct12.log D.npnct13.log D.npnct14.log
## 2537 1932 2582
## D.npnct15.log D.npnct16.log D.npnct17.log
## 2637 2546 2657
## D.npnct18.log D.npnct19.log D.npnct20.log
## 2656 2657 2657
## D.npnct21.log D.npnct22.log D.npnct23.log
## 2657 2657 2657
## D.npnct24.log D.npnct25.log D.npnct26.log
## 1520 2657 2657
## D.npnct27.log D.npnct28.log D.npnct29.log
## 2657 2649 2657
## D.npnct30.log D.nstopwrds.log D.P.http
## 2657 1663 2657
## D.P.mini D.P.air
## 2623 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.TfIdf.sum.post.stop
## 1597 1521 1521
## D.terms.n.post.stem D.TfIdf.sum.post.stem D.T.condit
## 1521 1521 2162
## D.T.use D.T.scratch D.T.new
## 2367 2371 2501
## D.T.good D.T.screen D.T.ipad
## 2460 2444 2425
## D.T.great D.T.work D.T.excel
## 2532 2459 2558
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 1520 1521 1521
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log
## 1521 1520 1522
## D.ndgts.log D.npnct01.log D.npnct02.log
## 2426 2579 2657
## D.npnct03.log D.npnct04.log D.npnct05.log
## 2614 2657 2592
## D.npnct06.log D.npnct07.log D.npnct08.log
## 2554 2656 2581
## D.npnct09.log D.npnct10.log D.npnct11.log
## 2641 2648 2301
## D.npnct12.log D.npnct13.log D.npnct14.log
## 2537 1932 2582
## D.npnct15.log D.npnct16.log D.npnct17.log
## 2637 2546 2657
## D.npnct18.log D.npnct19.log D.npnct20.log
## 2656 2657 2657
## D.npnct21.log D.npnct22.log D.npnct23.log
## 2657 2657 2657
## D.npnct24.log D.npnct25.log D.npnct26.log
## 1520 2657 2657
## D.npnct27.log D.npnct28.log D.npnct29.log
## 2657 2649 2657
## D.npnct30.log D.nstopwrds.log D.P.http
## 2657 1663 2657
## D.P.mini D.P.air
## 2623 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print("Clustering features: ")
print(cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glb_txt_vars, 1, 1), collapse="")),
"]\\.[PT]\\."),
names(glb_allobs_df), value=TRUE))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_rsp_var]), method="ML")))
category_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, glb_rsp_var]))
names(category_df)[c(1, 2)] <- c(glb_category_var, glb_rsp_var)
category_df <- do.call(tidyr::spread, list(category_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(category_df),
function(row) entropy(as.numeric(category_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(category_df),
function(row) sum(as.numeric(category_df[row, -1])))
category_df$.entropy <- tmp.entropy; category_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)", glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
print(category_df)
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (grp in sort(unique(glb_allobs_df[, glb_category_var]))) {
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df[, glb_category_var].fctr) / minClusterSize=20)
# which(levels(glb_allobs_df[, glb_category_var].fctr) == grp)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df[, glb_category_var]==grp,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
cluster_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, ".clusterid"],
glb_allobs_df[, glb_rsp_var]))
cluster_df <- subset(cluster_df, Freq > 0)
names(cluster_df)[c(1, 2, 3)] <- c(glb_category_var, ".clusterid", glb_rsp_var)
# spread(unite(cluster_df, prdline.my.clusterid, prdline.my, .clusterid),
# sold.fctr, Freq)
cluster_df <- do.call(tidyr::unite,
list(cluster_df, paste0(glb_category_var, ".clusterid"),
grep(glb_category_var, names(cluster_df)),
grep(".clusterid", names(cluster_df))))
cluster_df <- do.call(tidyr::spread,
list(cluster_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
print(cluster_df)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features[paste0(glb_category_var, ".fctr")] <-
c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr## [1] "Clustering features: "
## [1] "D.T.condit" "D.T.use" "D.T.scratch" "D.T.new" "D.T.good"
## [6] "D.T.screen" "D.T.ipad" "D.T.great" "D.T.work" "D.T.excel"
## [11] "D.P.http" "D.P.mini" "D.P.air"
## [1] "glb_allobs_df Entropy: 0.6903"
## [1] "glb_allobs_df$prdline.my Entropy: 0.6850 (99.2280 pct)"
## prdline.my N Y .entropy .knt
## 1 Unknown 118 80 0.6746159 198
## 2 iPad 1 100 125 0.6869616 225
## 3 iPad 2 141 147 0.6929302 288
## 4 iPad 3+ 166 145 0.6908657 311
## 5 iPadAir 203 150 0.6818332 353
## 6 iPadmini 146 133 0.6920612 279
## 7 iPadmini 2+ 125 80 0.6688571 205
## [1] "Category: Unknown"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 5 10005 N Unknown
## 130 10130 Y Unknown
## descr.my
## 5 Please feel free to buy. All product have been thoroughly inspected, cleaned and tested to be 100%
## 130 New - Open Box. Charger included.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 5 0 0 0 0.0000000 0 0 0
## 130 0 0 0 0.8180361 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 5 0 0 0 0 0 0
## 130 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 389 10389 Y Unknown
## 1618 11619 Y Unknown
## descr.my
## 389 Working condition!
## 1618 Please note that this unit is in working condition, but is locked with another Apple ID
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 389 1.2121489 0 0 0 0 0 0
## 1618 0.3030372 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 389 0 1.8731129 0 0 0 0
## 1618 0 0.4682782 0 0 0 0
## [1] "Category: iPad 1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 9 10009 Y iPad 1
## 13 10013 Y iPad 1
## descr.my
## 9
## 13 GOOD CONDITION. CLEAN ICLOUD. NO LOCKS. CLEAN IMEI. This tablet has been fully tested and works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 9 0.0000000 0 0 0 0.0000000 0 0
## 13 0.2203907 0 0 0 0.3412301 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 9 0 0.000000 0 0 0 0
## 13 0 0.340566 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my descr.my
## 165 10165 Y iPad 1 Excellent condition see pictures.
## 1980 11982 <NA> iPad 1 Excellent condition see pictures.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 165 0.6060744 0 0 0 0 0 0
## 1980 0.6060744 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 165 0 0 1.186556 0 0 0
## 1980 0 0 1.186556 0 0 0
## [1] "Category: iPad 2"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 1 10001 N iPad 2
## 2 10002 Y iPad 2
## descr.my
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 1 0.8080993 0.0000000 0.0000000 0 0 0.0000000 1.172534
## 2 0.0000000 0.5810315 0.2923374 0 0 0.3309884 0.000000
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 21 10021 Y iPad 2
## 329 10329 Y iPad 2
## descr.my
## 21 Crack on screen.
## 329 Item has original packaging. Original packaging has stickers on box. Minor screen flaw (Does not
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 21 0 0 0 0 0 1.8204364 0
## 329 0 0 0 0 0 0.3640873 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 21 0 0 0 0 0 0
## 329 0 0 0 0 0 0
## [1] "Category: iPad 3+"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 3 10003 Y iPad 3+
## 11 10011 Y iPad 3+
## descr.my
## 3
## 11 good condition, minor wear and tear on body some light scratches on screen. functions great.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 3 0.0000000 0 0.0000000 0 0.0000000 0.0000000 0
## 11 0.2203907 0 0.2923374 0 0.3412301 0.3309884 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 3 0.0000000 0 0 0 0 0
## 11 0.4008907 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 40 10040 N iPad 3+
## 1602 11603 Y iPad 3+
## descr.my
## 40 Item has been professionally tested and inspected. Tests show that all features work correctly. This
## 1602 Work fine iCloud lock
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 40 0 0 0 0 0 0 0
## 1602 0 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 40 0 0.4162473 0 0 0 0
## 1602 0 0.9365565 0 0 0 0
## [1] "Category: iPadAir"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 16 10016 N iPadAir
## 33 10033 N iPadAir
## descr.my
## 16
## 33 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 16 0 0 0 0 0.000000 0 0.0000000
## 33 0 0 0 0 0.417059 0 0.3908446
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 16 0 0 0 0 0 0
## 33 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 96 10096 N iPadAir
## 461 10461 N iPadAir
## descr.my
## 96 Open Box Excellent Condition . All original accessories included in its original box.
## 461 Excellent condition.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 96 0.2693664 0 0 0 0 0 0
## 461 1.2121489 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 96 0 0 0.5273584 0 0 0
## 461 0 0 2.3731129 0 0 0
## [1] "Category: iPadmini"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 7 10007 Y iPadmini
## 76 10076 Y iPadmini
## descr.my
## 7
## 76 Works perfectly, NOT iCloud locked, 1 owner. It is in not in very good condition, but works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 7 0.0000000 0 0 0 0.0000000 0 0
## 76 0.3030372 0 0 0 0.4691913 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 7 0 0.0000000 0 0 0 0
## 76 0 0.9365565 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 384 10384 Y iPadmini
## 2259 12261 <NA> iPadmini
## descr.my
## 384 Great condition and lightly used!
## 2259 I'm great condition! Rarely ever been used and has always been in a case.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 384 0.6060744 0.7989184 0 0 0 0 0
## 2259 0.3463283 0.4565248 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 384 1.1024496 0 0 0 0 0
## 2259 0.6299712 0 0 0 0 0
## [1] "Category: iPadmini 2+"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 4 10004 N iPadmini 2+
## 18 10018 N iPadmini 2+
## descr.my
## 4
## 18 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 4 0 0 0 0 0.000000 0 0.0000000
## 18 0 0 0 0 0.417059 0 0.3908446
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 4 0 0 0 0 0 0
## 18 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 750 10750 Y iPadmini 2+
## 2289 12291 <NA> iPadmini 2+
## descr.my
## 750 Slight crack on very top screen.
## 2289 Has a cracked screen and has always been in an otter box.I had removed case to clean and my toddler
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.screen D.T.ipad
## 750 0 0 0 0 0 0.9102182 0
## 2289 0 0 0 0 0 0.4045414 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 750 0 0 0 0 0 0
## 2289 0 0 0 0 0 0
## [1] "glb_allobs_df$prdline.my$.clusterid Entropy: 0.6728 (98.2111 pct)"
## prdline.my.clusterid N Y .entropy .knt
## 1 Unknown_1 77 53 0.6760076 130
## 2 Unknown_2 26 22 0.6896709 48
## 3 Unknown_3 15 5 0.5623351 20
## 4 iPad 1_1 62 88 0.6780488 150
## 5 iPad 1_2 13 16 0.6877868 29
## 6 iPad 1_3 13 13 0.6931472 26
## 7 iPad 1_4 12 8 0.6730117 20
## 8 iPad 2_1 72 94 0.6843392 166
## 9 iPad 2_2 40 29 0.6803854 69
## 10 iPad 2_3 18 5 0.5235863 23
## 11 iPad 2_4 6 11 0.6492484 17
## 12 iPad 2_5 5 8 0.6662784 13
## 13 iPad 3+_1 80 100 0.6869616 180
## 14 iPad 3+_2 46 14 0.5432728 60
## 15 iPad 3+_3 23 16 0.6769517 39
## 16 iPad 3+_4 17 15 0.6911928 32
## 17 iPadAir_1 133 101 0.6837672 234
## 18 iPadAir_2 52 41 0.6861358 93
## 19 iPadAir_3 18 8 0.6172418 26
## 20 iPadmini 2+_1 100 61 0.6635142 161
## 21 iPadmini 2+_2 11 9 0.6881388 20
## 22 iPadmini 2+_3 14 10 0.6791933 24
## 23 iPadmini_1 99 87 0.6910646 186
## 24 iPadmini_2 17 14 0.6884572 31
## 25 iPadmini_3 11 18 0.6637255 29
## 26 iPadmini_4 12 10 0.6890092 22
## 27 iPadmini_5 7 4 0.6554818 11# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 77.941 81.458 3.517
## 8 select.features 5 0 81.459 NA NAStep 5.0: select features
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## sold sold 1.0000000000
## biddable biddable 0.5481788380
## startprice.log startprice.log -0.4674275376
## startprice startprice -0.4569767211
## UniqueID UniqueID -0.1895466260
## idseq.my idseq.my -0.1895466260
## condition.fctr condition.fctr -0.1535490071
## D.npnct05.log D.npnct05.log -0.1180558939
## D.terms.n.post.stop D.terms.n.post.stop -0.0832265597
## D.terms.n.post.stem D.terms.n.post.stem -0.0830663021
## D.npnct14.log D.npnct14.log -0.0786203827
## cellular.fctr cellular.fctr -0.0730394149
## carrier.fctr carrier.fctr -0.0685230948
## D.nwrds.unq.log D.nwrds.unq.log -0.0651243063
## D.ndgts.log D.ndgts.log -0.0637072932
## D.npnct09.log D.npnct09.log -0.0618253281
## D.nwrds.log D.nwrds.log -0.0590599485
## D.npnct12.log D.npnct12.log -0.0568348971
## D.nchrs.log D.nchrs.log -0.0565292623
## D.ratio.nstopwrds.nwrds D.ratio.nstopwrds.nwrds 0.0556485120
## D.nuppr.log D.nuppr.log -0.0553820542
## D.npnct28.log D.npnct28.log -0.0524583244
## D.npnct06.log D.npnct06.log -0.0499761958
## D.npnct15.log D.npnct15.log 0.0484022793
## D.nstopwrds.log D.nstopwrds.log -0.0464404483
## D.npnct24.log D.npnct24.log -0.0458449965
## D.npnct16.log D.npnct16.log -0.0449403962
## color.fctr color.fctr -0.0433350320
## prdline.my.fctr prdline.my.fctr -0.0415814340
## D.npnct08.log D.npnct08.log -0.0396513123
## D.T.new D.T.new -0.0383561562
## D.npnct13.log D.npnct13.log -0.0373463069
## D.T.condit D.T.condit -0.0371486339
## D.TfIdf.sum.post.stem D.TfIdf.sum.post.stem -0.0351644554
## D.sum.TfIdf D.sum.TfIdf -0.0351644554
## .clusterid .clusterid -0.0349960850
## .clusterid.fctr .clusterid.fctr -0.0349960850
## D.TfIdf.sum.post.stop D.TfIdf.sum.post.stop -0.0325541826
## D.T.excel D.T.excel 0.0265752158
## D.npnct03.log D.npnct03.log 0.0257637868
## D.npnct07.log D.npnct07.log 0.0250040676
## D.T.screen D.T.screen 0.0242145889
## D.npnct10.log D.npnct10.log -0.0241015016
## D.npnct18.log D.npnct18.log -0.0215250231
## D.npnct11.log D.npnct11.log -0.0192035548
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 0.0165731742
## D.T.ipad D.T.ipad -0.0164082423
## D.T.work D.T.work -0.0124718624
## D.T.use D.T.use 0.0113062650
## D.P.mini D.P.mini -0.0112418293
## storage.fctr storage.fctr -0.0110508589
## D.P.air D.P.air -0.0092629952
## D.ratio.sum.TfIdf.nwrds D.ratio.sum.TfIdf.nwrds 0.0082354655
## D.TfIdf.sum.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio -0.0078908915
## D.T.great D.T.great 0.0078649564
## D.T.scratch D.T.scratch -0.0072207615
## .rnorm .rnorm 0.0067562740
## D.npnct01.log D.npnct01.log 0.0041255300
## D.T.good D.T.good -0.0005088937
## D.npnct02.log D.npnct02.log NA
## D.npnct04.log D.npnct04.log NA
## D.npnct17.log D.npnct17.log NA
## D.npnct19.log D.npnct19.log NA
## D.npnct20.log D.npnct20.log NA
## D.npnct21.log D.npnct21.log NA
## D.npnct22.log D.npnct22.log NA
## D.npnct23.log D.npnct23.log NA
## D.npnct25.log D.npnct25.log NA
## D.npnct26.log D.npnct26.log NA
## D.npnct27.log D.npnct27.log NA
## D.npnct29.log D.npnct29.log NA
## D.npnct30.log D.npnct30.log NA
## D.P.http D.P.http NA
## exclude.as.feat cor.y.abs
## sold 1 1.0000000000
## biddable 0 0.5481788380
## startprice.log 0 0.4674275376
## startprice 1 0.4569767211
## UniqueID 1 0.1895466260
## idseq.my 0 0.1895466260
## condition.fctr 0 0.1535490071
## D.npnct05.log 0 0.1180558939
## D.terms.n.post.stop 0 0.0832265597
## D.terms.n.post.stem 0 0.0830663021
## D.npnct14.log 0 0.0786203827
## cellular.fctr 0 0.0730394149
## carrier.fctr 0 0.0685230948
## D.nwrds.unq.log 0 0.0651243063
## D.ndgts.log 0 0.0637072932
## D.npnct09.log 0 0.0618253281
## D.nwrds.log 0 0.0590599485
## D.npnct12.log 0 0.0568348971
## D.nchrs.log 0 0.0565292623
## D.ratio.nstopwrds.nwrds 0 0.0556485120
## D.nuppr.log 0 0.0553820542
## D.npnct28.log 0 0.0524583244
## D.npnct06.log 0 0.0499761958
## D.npnct15.log 0 0.0484022793
## D.nstopwrds.log 0 0.0464404483
## D.npnct24.log 0 0.0458449965
## D.npnct16.log 0 0.0449403962
## color.fctr 0 0.0433350320
## prdline.my.fctr 0 0.0415814340
## D.npnct08.log 0 0.0396513123
## D.T.new 1 0.0383561562
## D.npnct13.log 0 0.0373463069
## D.T.condit 1 0.0371486339
## D.TfIdf.sum.post.stem 0 0.0351644554
## D.sum.TfIdf 0 0.0351644554
## .clusterid 1 0.0349960850
## .clusterid.fctr 0 0.0349960850
## D.TfIdf.sum.post.stop 0 0.0325541826
## D.T.excel 1 0.0265752158
## D.npnct03.log 0 0.0257637868
## D.npnct07.log 0 0.0250040676
## D.T.screen 1 0.0242145889
## D.npnct10.log 0 0.0241015016
## D.npnct18.log 0 0.0215250231
## D.npnct11.log 0 0.0192035548
## D.terms.n.stem.stop.Ratio 0 0.0165731742
## D.T.ipad 1 0.0164082423
## D.T.work 1 0.0124718624
## D.T.use 1 0.0113062650
## D.P.mini 1 0.0112418293
## storage.fctr 0 0.0110508589
## D.P.air 1 0.0092629952
## D.ratio.sum.TfIdf.nwrds 0 0.0082354655
## D.TfIdf.sum.stem.stop.Ratio 0 0.0078908915
## D.T.great 1 0.0078649564
## D.T.scratch 1 0.0072207615
## .rnorm 0 0.0067562740
## D.npnct01.log 0 0.0041255300
## D.T.good 1 0.0005088937
## D.npnct02.log 0 NA
## D.npnct04.log 0 NA
## D.npnct17.log 0 NA
## D.npnct19.log 0 NA
## D.npnct20.log 0 NA
## D.npnct21.log 0 NA
## D.npnct22.log 0 NA
## D.npnct23.log 0 NA
## D.npnct25.log 0 NA
## D.npnct26.log 0 NA
## D.npnct27.log 0 NA
## D.npnct29.log 0 NA
## D.npnct30.log 0 NA
## D.P.http 1 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(D.TfIdf.sum.post.stem, D.sum.TfIdf)=1.0000"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0352"
## [1] "cor(sold.fctr, D.sum.TfIdf)=-0.0352"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.sum.TfIdf as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nchrs.log, D.nuppr.log)=0.9995"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0565"
## [1] "cor(sold.fctr, D.nuppr.log)=-0.0554"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nuppr.log as highly correlated with
## D.nchrs.log## [1] "cor(D.terms.n.post.stem, D.terms.n.post.stop)=0.9991"
## [1] "cor(sold.fctr, D.terms.n.post.stem)=-0.0831"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0832"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.TfIdf.sum.post.stem, D.TfIdf.sum.post.stop)=0.9977"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0352"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stop)=-0.0326"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stop as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nchrs.log, D.nwrds.unq.log)=0.9934"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0565"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0651"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nchrs.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.nwrds.log, D.nwrds.unq.log)=0.9927"
## [1] "cor(sold.fctr, D.nwrds.log)=-0.0591"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0651"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stop)=0.9755"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0651"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0832"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.unq.log as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.npnct24.log, D.ratio.nstopwrds.nwrds)=-0.9649"
## [1] "cor(sold.fctr, D.npnct24.log)=-0.0458"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0556"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct24.log as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.npnct06.log, D.npnct16.log)=0.9556"
## [1] "cor(sold.fctr, D.npnct06.log)=-0.0500"
## [1] "cor(sold.fctr, D.npnct16.log)=-0.0449"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct16.log as highly correlated with
## D.npnct06.log## [1] "cor(D.TfIdf.sum.post.stem, D.ratio.nstopwrds.nwrds)=-0.9261"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0352"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0556"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stem as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.nstopwrds.log, D.terms.n.post.stop)=0.8952"
## [1] "cor(sold.fctr, D.nstopwrds.log)=-0.0464"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0832"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nstopwrds.log as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.ratio.nstopwrds.nwrds, D.terms.n.post.stop)=-0.8682"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0556"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0832"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.nstopwrds.nwrds as highly correlated
## with D.terms.n.post.stop## [1] "cor(carrier.fctr, cellular.fctr)=0.8461"
## [1] "cor(sold.fctr, carrier.fctr)=-0.0685"
## [1] "cor(sold.fctr, cellular.fctr)=-0.0730"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr## [1] "cor(D.npnct13.log, D.terms.n.post.stop)=0.7388"
## [1] "cor(sold.fctr, D.npnct13.log)=-0.0373"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0832"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct13.log as highly correlated with
## D.terms.n.post.stop## id cor.y exclude.as.feat cor.y.abs
## 70 sold 1.0000000000 1 1.0000000000
## 63 biddable 0.5481788380 0 0.5481788380
## 56 D.ratio.nstopwrds.nwrds 0.0556485120 0 0.0556485120
## 36 D.npnct15.log 0.0484022793 0 0.0484022793
## 8 D.T.excel 0.0265752158 1 0.0265752158
## 24 D.npnct03.log 0.0257637868 0 0.0257637868
## 28 D.npnct07.log 0.0250040676 0 0.0250040676
## 14 D.T.screen 0.0242145889 1 0.0242145889
## 61 D.terms.n.stem.stop.Ratio 0.0165731742 0 0.0165731742
## 15 D.T.use 0.0113062650 1 0.0113062650
## 57 D.ratio.sum.TfIdf.nwrds 0.0082354655 0 0.0082354655
## 10 D.T.great 0.0078649564 1 0.0078649564
## 3 .rnorm 0.0067562740 0 0.0067562740
## 22 D.npnct01.log 0.0041255300 0 0.0041255300
## 9 D.T.good -0.0005088937 1 0.0005088937
## 13 D.T.scratch -0.0072207615 1 0.0072207615
## 19 D.TfIdf.sum.stem.stop.Ratio -0.0078908915 0 0.0078908915
## 4 D.P.air -0.0092629952 1 0.0092629952
## 73 storage.fctr -0.0110508589 0 0.0110508589
## 6 D.P.mini -0.0112418293 1 0.0112418293
## 16 D.T.work -0.0124718624 1 0.0124718624
## 11 D.T.ipad -0.0164082423 1 0.0164082423
## 32 D.npnct11.log -0.0192035548 0 0.0192035548
## 39 D.npnct18.log -0.0215250231 0 0.0215250231
## 31 D.npnct10.log -0.0241015016 0 0.0241015016
## 18 D.TfIdf.sum.post.stop -0.0325541826 0 0.0325541826
## 1 .clusterid -0.0349960850 1 0.0349960850
## 2 .clusterid.fctr -0.0349960850 0 0.0349960850
## 17 D.TfIdf.sum.post.stem -0.0351644554 0 0.0351644554
## 58 D.sum.TfIdf -0.0351644554 0 0.0351644554
## 7 D.T.condit -0.0371486339 1 0.0371486339
## 34 D.npnct13.log -0.0373463069 0 0.0373463069
## 12 D.T.new -0.0383561562 1 0.0383561562
## 29 D.npnct08.log -0.0396513123 0 0.0396513123
## 69 prdline.my.fctr -0.0415814340 0 0.0415814340
## 66 color.fctr -0.0433350320 0 0.0433350320
## 37 D.npnct16.log -0.0449403962 0 0.0449403962
## 45 D.npnct24.log -0.0458449965 0 0.0458449965
## 52 D.nstopwrds.log -0.0464404483 0 0.0464404483
## 27 D.npnct06.log -0.0499761958 0 0.0499761958
## 49 D.npnct28.log -0.0524583244 0 0.0524583244
## 53 D.nuppr.log -0.0553820542 0 0.0553820542
## 20 D.nchrs.log -0.0565292623 0 0.0565292623
## 33 D.npnct12.log -0.0568348971 0 0.0568348971
## 54 D.nwrds.log -0.0590599485 0 0.0590599485
## 30 D.npnct09.log -0.0618253281 0 0.0618253281
## 21 D.ndgts.log -0.0637072932 0 0.0637072932
## 55 D.nwrds.unq.log -0.0651243063 0 0.0651243063
## 64 carrier.fctr -0.0685230948 0 0.0685230948
## 65 cellular.fctr -0.0730394149 0 0.0730394149
## 35 D.npnct14.log -0.0786203827 0 0.0786203827
## 59 D.terms.n.post.stem -0.0830663021 0 0.0830663021
## 60 D.terms.n.post.stop -0.0832265597 0 0.0832265597
## 26 D.npnct05.log -0.1180558939 0 0.1180558939
## 67 condition.fctr -0.1535490071 0 0.1535490071
## 62 UniqueID -0.1895466260 1 0.1895466260
## 68 idseq.my -0.1895466260 0 0.1895466260
## 71 startprice -0.4569767211 1 0.4569767211
## 72 startprice.log -0.4674275376 0 0.4674275376
## 5 D.P.http NA 1 NA
## 23 D.npnct02.log NA 0 NA
## 25 D.npnct04.log NA 0 NA
## 38 D.npnct17.log NA 0 NA
## 40 D.npnct19.log NA 0 NA
## 41 D.npnct20.log NA 0 NA
## 42 D.npnct21.log NA 0 NA
## 43 D.npnct22.log NA 0 NA
## 44 D.npnct23.log NA 0 NA
## 46 D.npnct25.log NA 0 NA
## 47 D.npnct26.log NA 0 NA
## 48 D.npnct27.log NA 0 NA
## 50 D.npnct29.log NA 0 NA
## 51 D.npnct30.log NA 0 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## 70 <NA> 1.161628 0.10758472 FALSE FALSE
## 63 <NA> 1.221027 0.10758472 FALSE FALSE
## 56 D.terms.n.post.stop 13.375000 4.19580420 FALSE FALSE
## 36 <NA> 153.416667 0.16137708 FALSE TRUE
## 8 <NA> 138.153846 0.75309306 FALSE TRUE
## 24 <NA> 83.227273 0.16137708 FALSE TRUE
## 28 <NA> 1858.000000 0.10758472 FALSE TRUE
## 14 <NA> 55.064516 0.80688542 FALSE TRUE
## 61 <NA> 77.826087 0.48413125 FALSE TRUE
## 15 <NA> 47.228571 0.91447015 FALSE TRUE
## 57 <NA> 63.000000 34.85745024 FALSE FALSE
## 10 <NA> 104.470588 0.80688542 FALSE TRUE
## 3 <NA> 1.000000 100.00000000 FALSE FALSE
## 22 <NA> 52.970588 0.32275417 FALSE TRUE
## 9 <NA> 47.833333 0.86067778 FALSE TRUE
## 13 <NA> 40.390244 0.86067778 FALSE TRUE
## 19 <NA> 65.176471 32.92092523 FALSE FALSE
## 4 <NA> 122.866667 0.16137708 FALSE TRUE
## 73 <NA> 2.733138 0.26896181 FALSE FALSE
## 6 <NA> 91.900000 0.16137708 FALSE TRUE
## 16 <NA> 61.357143 0.69930070 FALSE TRUE
## 11 <NA> 49.823529 0.80688542 FALSE TRUE
## 32 <NA> 9.374269 0.37654653 FALSE FALSE
## 39 <NA> 1858.000000 0.10758472 FALSE TRUE
## 31 <NA> 308.666667 0.16137708 FALSE TRUE
## 18 D.TfIdf.sum.post.stem 63.000000 34.37331899 FALSE FALSE
## 1 <NA> 3.448571 0.26896181 FALSE FALSE
## 2 <NA> 3.448571 0.26896181 FALSE FALSE
## 17 D.ratio.nstopwrds.nwrds 63.000000 34.31952663 FALSE FALSE
## 58 D.TfIdf.sum.post.stem 63.000000 34.31952663 FALSE FALSE
## 7 <NA> 24.868852 0.91447015 FALSE TRUE
## 34 D.terms.n.post.stop 5.203065 0.32275417 FALSE FALSE
## 12 <NA> 103.000000 0.86067778 FALSE TRUE
## 29 <NA> 69.576923 0.21516945 FALSE TRUE
## 69 <NA> 1.135048 0.37654653 FALSE FALSE
## 66 <NA> 1.574610 0.26896181 FALSE FALSE
## 37 D.npnct06.log 31.245614 0.16137708 FALSE TRUE
## 45 D.ratio.nstopwrds.nwrds 1.356147 0.10758472 FALSE FALSE
## 52 D.terms.n.post.stop 13.436782 0.80688542 FALSE FALSE
## 27 <NA> 33.735849 0.16137708 FALSE TRUE
## 49 <NA> 463.250000 0.16137708 FALSE TRUE
## 53 D.nchrs.log 18.807018 4.41097364 FALSE FALSE
## 20 D.nwrds.unq.log 15.970149 5.70199032 FALSE FALSE
## 33 <NA> 26.818182 0.21516945 FALSE TRUE
## 54 D.nwrds.unq.log 12.891566 1.29101668 FALSE FALSE
## 30 <NA> 308.333333 0.21516945 FALSE TRUE
## 21 <NA> 27.031746 0.69930070 FALSE TRUE
## 55 D.terms.n.post.stop 7.650000 0.80688542 FALSE FALSE
## 64 cellular.fctr 3.186782 0.37654653 FALSE FALSE
## 65 <NA> 2.116412 0.16137708 FALSE FALSE
## 35 <NA> 35.333333 0.26896181 FALSE TRUE
## 59 D.terms.n.post.stop 7.650000 0.80688542 FALSE FALSE
## 60 <NA> 8.175573 0.80688542 FALSE FALSE
## 26 <NA> 40.311111 0.10758472 FALSE TRUE
## 67 <NA> 4.003460 0.32275417 FALSE FALSE
## 62 <NA> 1.000000 100.00000000 FALSE FALSE
## 68 <NA> 1.000000 100.00000000 FALSE FALSE
## 71 <NA> 2.807692 30.17751479 FALSE FALSE
## 72 <NA> 2.807692 30.17751479 FALSE FALSE
## 5 <NA> 0.000000 0.05379236 TRUE TRUE
## 23 <NA> 0.000000 0.05379236 TRUE TRUE
## 25 <NA> 0.000000 0.05379236 TRUE TRUE
## 38 <NA> 0.000000 0.05379236 TRUE TRUE
## 40 <NA> 0.000000 0.05379236 TRUE TRUE
## 41 <NA> 0.000000 0.05379236 TRUE TRUE
## 42 <NA> 0.000000 0.05379236 TRUE TRUE
## 43 <NA> 0.000000 0.05379236 TRUE TRUE
## 44 <NA> 0.000000 0.05379236 TRUE TRUE
## 46 <NA> 0.000000 0.05379236 TRUE TRUE
## 47 <NA> 0.000000 0.05379236 TRUE TRUE
## 48 <NA> 0.000000 0.05379236 TRUE TRUE
## 50 <NA> 0.000000 0.05379236 TRUE TRUE
## 51 <NA> 0.000000 0.05379236 TRUE TRUE
## myNearZV is.cor.y.abs.low
## 70 FALSE FALSE
## 63 FALSE FALSE
## 56 FALSE FALSE
## 36 FALSE FALSE
## 8 FALSE FALSE
## 24 FALSE FALSE
## 28 TRUE FALSE
## 14 FALSE FALSE
## 61 FALSE FALSE
## 15 FALSE FALSE
## 57 FALSE FALSE
## 10 FALSE FALSE
## 3 FALSE FALSE
## 22 FALSE TRUE
## 9 FALSE TRUE
## 13 FALSE FALSE
## 19 FALSE FALSE
## 4 FALSE FALSE
## 73 FALSE FALSE
## 6 FALSE FALSE
## 16 FALSE FALSE
## 11 FALSE FALSE
## 32 FALSE FALSE
## 39 TRUE FALSE
## 31 FALSE FALSE
## 18 FALSE FALSE
## 1 FALSE FALSE
## 2 FALSE FALSE
## 17 FALSE FALSE
## 58 FALSE FALSE
## 7 FALSE FALSE
## 34 FALSE FALSE
## 12 FALSE FALSE
## 29 FALSE FALSE
## 69 FALSE FALSE
## 66 FALSE FALSE
## 37 FALSE FALSE
## 45 FALSE FALSE
## 52 FALSE FALSE
## 27 FALSE FALSE
## 49 FALSE FALSE
## 53 FALSE FALSE
## 20 FALSE FALSE
## 33 FALSE FALSE
## 54 FALSE FALSE
## 30 FALSE FALSE
## 21 FALSE FALSE
## 55 FALSE FALSE
## 64 FALSE FALSE
## 65 FALSE FALSE
## 35 FALSE FALSE
## 59 FALSE FALSE
## 60 FALSE FALSE
## 26 FALSE FALSE
## 67 FALSE FALSE
## 62 FALSE FALSE
## 68 FALSE FALSE
## 71 FALSE FALSE
## 72 FALSE FALSE
## 5 TRUE NA
## 23 TRUE NA
## 25 TRUE NA
## 38 TRUE NA
## 40 TRUE NA
## 41 TRUE NA
## 42 TRUE NA
## 43 TRUE NA
## 44 TRUE NA
## 46 TRUE NA
## 47 TRUE NA
## 48 TRUE NA
## 50 TRUE NA
## 51 TRUE NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 10 rows containing missing values (geom_point).## Warning: Removed 10 rows containing missing values (geom_point).## Warning: Removed 10 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 28 D.npnct07.log 0.02500407 0 0.02500407 <NA>
## 39 D.npnct18.log -0.02152502 0 0.02152502 <NA>
## 5 D.P.http NA 1 NA <NA>
## 23 D.npnct02.log NA 0 NA <NA>
## 25 D.npnct04.log NA 0 NA <NA>
## 38 D.npnct17.log NA 0 NA <NA>
## 40 D.npnct19.log NA 0 NA <NA>
## 41 D.npnct20.log NA 0 NA <NA>
## 42 D.npnct21.log NA 0 NA <NA>
## 43 D.npnct22.log NA 0 NA <NA>
## 44 D.npnct23.log NA 0 NA <NA>
## 46 D.npnct25.log NA 0 NA <NA>
## 47 D.npnct26.log NA 0 NA <NA>
## 48 D.npnct27.log NA 0 NA <NA>
## 50 D.npnct29.log NA 0 NA <NA>
## 51 D.npnct30.log NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 28 1858 0.10758472 FALSE TRUE TRUE FALSE
## 39 1858 0.10758472 FALSE TRUE TRUE FALSE
## 5 0 0.05379236 TRUE TRUE TRUE NA
## 23 0 0.05379236 TRUE TRUE TRUE NA
## 25 0 0.05379236 TRUE TRUE TRUE NA
## 38 0 0.05379236 TRUE TRUE TRUE NA
## 40 0 0.05379236 TRUE TRUE TRUE NA
## 41 0 0.05379236 TRUE TRUE TRUE NA
## 42 0 0.05379236 TRUE TRUE TRUE NA
## 43 0 0.05379236 TRUE TRUE TRUE NA
## 44 0 0.05379236 TRUE TRUE TRUE NA
## 46 0 0.05379236 TRUE TRUE TRUE NA
## 47 0 0.05379236 TRUE TRUE TRUE NA
## 48 0 0.05379236 TRUE TRUE TRUE NA
## 50 0 0.05379236 TRUE TRUE TRUE NA
## 51 0 0.05379236 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.TfIdf.sum.post.stop
## 1597 1521 1521
## D.terms.n.post.stem D.TfIdf.sum.post.stem D.T.condit
## 1521 1521 2162
## D.T.use D.T.scratch D.T.new
## 2367 2371 2501
## D.T.good D.T.screen D.T.ipad
## 2460 2444 2425
## D.T.great D.T.work D.T.excel
## 2532 2459 2558
## D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 1520 1521 1521
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log
## 1521 1520 1522
## D.ndgts.log D.npnct01.log D.npnct03.log
## 2426 2579 2614
## D.npnct05.log D.npnct06.log D.npnct08.log
## 2592 2554 2581
## D.npnct09.log D.npnct10.log D.npnct11.log
## 2641 2648 2301
## D.npnct12.log D.npnct13.log D.npnct14.log
## 2537 1932 2582
## D.npnct15.log D.npnct16.log D.npnct24.log
## 2637 2546 1520
## D.npnct28.log D.nstopwrds.log D.P.mini
## 2649 1663 2623
## D.P.air
## 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 81.459 85.179 3.72
## 9 partition.data.training 6 0 85.179 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_var)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_var,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_var=allobs_freq_df[, glb_category_var])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_var),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: sampling
##
## Attaching package: 'sampling'
##
## The following objects are masked from 'package:survival':
##
## cluster, strata
##
## The following object is masked from 'package:caret':
##
## clusterif (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## sold.0 sold.1 sold.NA
## NA NA 798
## Fit 524 450 NA
## OOB 475 410 NA
## sold.0 sold.1 sold.NA
## NA NA 1
## Fit 0.5379877 0.4620123 NA
## OOB 0.5367232 0.4632768 NA
## prdline.my .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 3 iPad 2 154 171 0.1929825 0.1932203
## 5 iPadAir 137 151 0.1716792 0.1706215
## 4 iPad 3+ 123 136 0.1541353 0.1536723
## 6 iPadmini 114 127 0.1428571 0.1435028
## 7 iPadmini 2+ 94 104 0.1177945 0.1175141
## 2 iPad 1 89 99 0.1115288 0.1118644
## 1 Unknown 87 97 0.1090226 0.1096045# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 73 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## sold.fctr sold.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 70 sold 1.0000000 TRUE 1.0000000 <NA>
## 62 UniqueID -0.1895466 TRUE 0.1895466 <NA>
## sold.fctr sold.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 70 1.161628 0.1075847 FALSE FALSE FALSE FALSE
## 62 1.000000 100.0000000 FALSE FALSE FALSE FALSE
## sold.fctr NA NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 70 <NA> TRUE NA NA
## 62 <NA> FALSE TRUE NA
## sold.fctr <NA> NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "D.npnct07.log" "D.npnct18.log" "D.P.http" "D.npnct02.log"
## [5] "D.npnct04.log" "D.npnct17.log" "D.npnct19.log" "D.npnct20.log"
## [9] "D.npnct21.log" "D.npnct22.log" "D.npnct23.log" "D.npnct25.log"
## [13] "D.npnct26.log" "D.npnct27.log" "D.npnct29.log" "D.npnct30.log"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2657 70print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 1859 69print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 974 69print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 885 69print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 798 69# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 85.179 86.041 0.863
## 10 fit.models 7 0 86.042 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(model_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.5379877 0.4620123
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379877 0.4620123
## 2 0.5379877 0.4620123
## 3 0.5379877 0.4620123
## 4 0.5379877 0.4620123
## 5 0.5379877 0.4620123
## 6 0.5379877 0.4620123
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 524
## 2 Y 450
## Prediction
## Reference N Y
## N 524 0
## Y 450 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379877e-01 0.000000e+00 5.060896e-01 5.696555e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.131410e-01 1.962860e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379877 0.4620123
## 2 0.5379877 0.4620123
## 3 0.5379877 0.4620123
## 4 0.5379877 0.4620123
## 5 0.5379877 0.4620123
## 6 0.5379877 0.4620123
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 475
## 2 Y 410
## Prediction
## Reference N Y
## N 475 0
## Y 410 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.367232e-01 0.000000e+00 5.032294e-01 5.699717e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.137716e-01 9.975777e-91
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.351 0.002 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.5379877
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060896 0.5696555 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5367232
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5032294 0.5699717 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6320225
## 4 0.3 0.6320225
## 5 0.4 0.6320225
## 6 0.5 0.4747253
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 524
## 2 Y 450
## Prediction
## Reference N Y
## N 0 524
## Y 0 450
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.620123e-01 0.000000e+00 4.303445e-01 4.939104e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 9.999991e-01 1.552393e-115
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6332046
## 4 0.3 0.6332046
## 5 0.4 0.6332046
## 6 0.5 0.4822521
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 475
## 2 Y 410
## Prediction
## Reference N Y
## N 0 475
## Y 0 410
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.632768e-01 0.000000e+00 4.300283e-01 4.967706e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 9.999948e-01 7.120214e-105
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.265 0.002 0.5071756
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.6320225 0.4620123
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.4303445 0.4939104 0 0.5191913
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6332046 0.4632768
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4300283 0.4967706 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: biddable, startprice.log"## Loading required package: rpart## Fitting cp = 0.511 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.5111111 0 1
##
## Node number 1: 974 observations
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.5379877 0.4620123) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 524
## 2 Y 450
## Prediction
## Reference N Y
## N 524 0
## Y 450 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379877e-01 0.000000e+00 5.060896e-01 5.696555e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.131410e-01 1.962860e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 475
## 2 Y 410
## Prediction
## Reference N Y
## N 475 0
## Y 410 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.367232e-01 0.000000e+00 5.032294e-01 5.699717e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.137716e-01 9.975777e-91
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart biddable, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.612 0.012
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.5379877
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060896 0.5696555 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5367232
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5032294 0.5699717 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: biddable, startprice.log"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.5111111111 0 1.0000000
## 2 0.0144444444 1 0.4888889
## 3 0.0133333333 3 0.4600000
## 4 0.0100000000 4 0.4466667
## 5 0.0055555556 6 0.4266667
## 6 0.0029629630 10 0.4044444
## 7 0.0008888889 13 0.3955556
## 8 0.0004444444 18 0.3911111
## 9 0.0003174603 23 0.3888889
## 10 0.0000000000 30 0.3866667
##
## Variable importance
## startprice.log biddable
## 50 50
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.1499, (0 missing)
## startprice.log < 4.610145 to the right, improve=117.2126, (0 missing)
## Surrogate splits:
## startprice.log < 5.030417 to the right, agree=0.751, adj=0.46, (0 split)
##
## Node number 2: 524 observations, complexity param=0.002962963
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
## left son=4 (276 obs) right son=5 (248 obs)
## Primary splits:
## startprice.log < 5.527188 to the right, improve=4.393115, (0 missing)
##
## Node number 3: 450 observations, complexity param=0.01444444
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (162 obs) right son=7 (288 obs)
## Primary splits:
## startprice.log < 4.863673 to the right, improve=38.02778, (0 missing)
##
## Node number 4: 276 observations, complexity param=0.0004444444
## predicted class=N expected loss=0.1485507 P(node) =0.2833676
## class counts: 235 41
## probabilities: 0.851 0.149
## left son=8 (119 obs) right son=9 (157 obs)
## Primary splits:
## startprice.log < 5.972491 to the right, improve=0.9523844, (0 missing)
##
## Node number 5: 248 observations, complexity param=0.002962963
## predicted class=N expected loss=0.2782258 P(node) =0.2546201
## class counts: 179 69
## probabilities: 0.722 0.278
## left son=10 (9 obs) right son=11 (239 obs)
## Primary splits:
## startprice.log < 3.365867 to the left, improve=1.445843, (0 missing)
##
## Node number 6: 162 observations, complexity param=0.01444444
## predicted class=N expected loss=0.4814815 P(node) =0.1663244
## class counts: 84 78
## probabilities: 0.519 0.481
## left son=12 (27 obs) right son=13 (135 obs)
## Primary splits:
## startprice.log < 6.024113 to the right, improve=3.2, (0 missing)
##
## Node number 7: 288 observations
## predicted class=Y expected loss=0.09027778 P(node) =0.2956879
## class counts: 26 262
## probabilities: 0.090 0.910
##
## Node number 8: 119 observations
## predicted class=N expected loss=0.1008403 P(node) =0.1221766
## class counts: 107 12
## probabilities: 0.899 0.101
##
## Node number 9: 157 observations, complexity param=0.0004444444
## predicted class=N expected loss=0.1847134 P(node) =0.161191
## class counts: 128 29
## probabilities: 0.815 0.185
## left son=18 (147 obs) right son=19 (10 obs)
## Primary splits:
## startprice.log < 5.940158 to the left, improve=2.123359, (0 missing)
##
## Node number 10: 9 observations
## predicted class=N expected loss=0 P(node) =0.009240246
## class counts: 9 0
## probabilities: 1.000 0.000
##
## Node number 11: 239 observations, complexity param=0.002962963
## predicted class=N expected loss=0.2887029 P(node) =0.2453799
## class counts: 170 69
## probabilities: 0.711 0.289
## left son=22 (225 obs) right son=23 (14 obs)
## Primary splits:
## startprice.log < 4.248424 to the right, improve=3.730424, (0 missing)
##
## Node number 12: 27 observations
## predicted class=N expected loss=0.2592593 P(node) =0.02772074
## class counts: 20 7
## probabilities: 0.741 0.259
##
## Node number 13: 135 observations, complexity param=0.01333333
## predicted class=Y expected loss=0.4740741 P(node) =0.1386037
## class counts: 64 71
## probabilities: 0.474 0.526
## left son=26 (10 obs) right son=27 (125 obs)
## Primary splits:
## startprice.log < 5.010535 to the left, improve=2.294519, (0 missing)
##
## Node number 18: 147 observations, complexity param=0.0004444444
## predicted class=N expected loss=0.1632653 P(node) =0.150924
## class counts: 123 24
## probabilities: 0.837 0.163
## left son=36 (39 obs) right son=37 (108 obs)
## Primary splits:
## startprice.log < 5.669864 to the left, improve=1.331356, (0 missing)
##
## Node number 19: 10 observations
## predicted class=N expected loss=0.5 P(node) =0.01026694
## class counts: 5 5
## probabilities: 0.500 0.500
##
## Node number 22: 225 observations, complexity param=0.0008888889
## predicted class=N expected loss=0.2666667 P(node) =0.2310062
## class counts: 165 60
## probabilities: 0.733 0.267
## left son=44 (209 obs) right son=45 (16 obs)
## Primary splits:
## startprice.log < 5.521381 to the left, improve=1.005383, (0 missing)
##
## Node number 23: 14 observations
## predicted class=Y expected loss=0.3571429 P(node) =0.01437372
## class counts: 5 9
## probabilities: 0.357 0.643
##
## Node number 26: 10 observations
## predicted class=N expected loss=0.2 P(node) =0.01026694
## class counts: 8 2
## probabilities: 0.800 0.200
##
## Node number 27: 125 observations, complexity param=0.01
## predicted class=Y expected loss=0.448 P(node) =0.1283368
## class counts: 56 69
## probabilities: 0.448 0.552
## left son=54 (98 obs) right son=55 (27 obs)
## Primary splits:
## startprice.log < 5.176086 to the right, improve=1.585149, (0 missing)
##
## Node number 36: 39 observations
## predicted class=N expected loss=0.05128205 P(node) =0.04004107
## class counts: 37 2
## probabilities: 0.949 0.051
##
## Node number 37: 108 observations, complexity param=0.0004444444
## predicted class=N expected loss=0.2037037 P(node) =0.110883
## class counts: 86 22
## probabilities: 0.796 0.204
## left son=74 (67 obs) right son=75 (41 obs)
## Primary splits:
## startprice.log < 5.768305 to the right, improve=1.046502, (0 missing)
##
## Node number 44: 209 observations, complexity param=0.0008888889
## predicted class=N expected loss=0.2535885 P(node) =0.2145791
## class counts: 156 53
## probabilities: 0.746 0.254
## left son=88 (185 obs) right son=89 (24 obs)
## Primary splits:
## startprice.log < 4.499754 to the right, improve=0.799347, (0 missing)
##
## Node number 45: 16 observations
## predicted class=N expected loss=0.4375 P(node) =0.0164271
## class counts: 9 7
## probabilities: 0.562 0.438
##
## Node number 54: 98 observations, complexity param=0.01
## predicted class=Y expected loss=0.4897959 P(node) =0.100616
## class counts: 48 50
## probabilities: 0.490 0.510
## left son=108 (67 obs) right son=109 (31 obs)
## Primary splits:
## startprice.log < 5.700393 to the left, improve=2.535682, (0 missing)
##
## Node number 55: 27 observations
## predicted class=Y expected loss=0.2962963 P(node) =0.02772074
## class counts: 8 19
## probabilities: 0.296 0.704
##
## Node number 74: 67 observations
## predicted class=N expected loss=0.1492537 P(node) =0.0687885
## class counts: 57 10
## probabilities: 0.851 0.149
##
## Node number 75: 41 observations, complexity param=0.0004444444
## predicted class=N expected loss=0.2926829 P(node) =0.04209446
## class counts: 29 12
## probabilities: 0.707 0.293
## left son=150 (30 obs) right son=151 (11 obs)
## Primary splits:
## startprice.log < 5.720131 to the left, improve=1.921064, (0 missing)
##
## Node number 88: 185 observations, complexity param=0.0008888889
## predicted class=N expected loss=0.2378378 P(node) =0.1899384
## class counts: 141 44
## probabilities: 0.762 0.238
## left son=176 (34 obs) right son=177 (151 obs)
## Primary splits:
## startprice.log < 4.828114 to the left, improve=1.864583, (0 missing)
##
## Node number 89: 24 observations
## predicted class=N expected loss=0.375 P(node) =0.02464066
## class counts: 15 9
## probabilities: 0.625 0.375
##
## Node number 108: 67 observations, complexity param=0.005555556
## predicted class=N expected loss=0.4328358 P(node) =0.0687885
## class counts: 38 29
## probabilities: 0.567 0.433
## left son=216 (9 obs) right son=217 (58 obs)
## Primary splits:
## startprice.log < 5.549046 to the right, improve=0.9223423, (0 missing)
##
## Node number 109: 31 observations
## predicted class=Y expected loss=0.3225806 P(node) =0.03182752
## class counts: 10 21
## probabilities: 0.323 0.677
##
## Node number 150: 30 observations
## predicted class=N expected loss=0.2 P(node) =0.03080082
## class counts: 24 6
## probabilities: 0.800 0.200
##
## Node number 151: 11 observations
## predicted class=Y expected loss=0.4545455 P(node) =0.01129363
## class counts: 5 6
## probabilities: 0.455 0.545
##
## Node number 176: 34 observations
## predicted class=N expected loss=0.08823529 P(node) =0.0349076
## class counts: 31 3
## probabilities: 0.912 0.088
##
## Node number 177: 151 observations, complexity param=0.0008888889
## predicted class=N expected loss=0.2715232 P(node) =0.1550308
## class counts: 110 41
## probabilities: 0.728 0.272
## left son=354 (130 obs) right son=355 (21 obs)
## Primary splits:
## startprice.log < 5.010602 to the right, improve=0.5841836, (0 missing)
##
## Node number 216: 9 observations
## predicted class=N expected loss=0.2222222 P(node) =0.009240246
## class counts: 7 2
## probabilities: 0.778 0.222
##
## Node number 217: 58 observations, complexity param=0.005555556
## predicted class=N expected loss=0.4655172 P(node) =0.05954825
## class counts: 31 27
## probabilities: 0.534 0.466
## left son=434 (45 obs) right son=435 (13 obs)
## Primary splits:
## startprice.log < 5.501177 to the left, improve=1.723607, (0 missing)
##
## Node number 354: 130 observations, complexity param=0.0003174603
## predicted class=N expected loss=0.2538462 P(node) =0.1334702
## class counts: 97 33
## probabilities: 0.746 0.254
## left son=708 (14 obs) right son=709 (116 obs)
## Primary splits:
## startprice.log < 5.074986 to the left, improve=0.3865479, (0 missing)
##
## Node number 355: 21 observations, complexity param=0.0008888889
## predicted class=N expected loss=0.3809524 P(node) =0.02156057
## class counts: 13 8
## probabilities: 0.619 0.381
## left son=710 (13 obs) right son=711 (8 obs)
## Primary splits:
## startprice.log < 4.961339 to the left, improve=1.539377, (0 missing)
##
## Node number 434: 45 observations, complexity param=0.005555556
## predicted class=N expected loss=0.4 P(node) =0.04620123
## class counts: 27 18
## probabilities: 0.600 0.400
## left son=868 (18 obs) right son=869 (27 obs)
## Primary splits:
## startprice.log < 5.322712 to the right, improve=1.896296, (0 missing)
##
## Node number 435: 13 observations
## predicted class=Y expected loss=0.3076923 P(node) =0.01334702
## class counts: 4 9
## probabilities: 0.308 0.692
##
## Node number 708: 14 observations
## predicted class=N expected loss=0.1428571 P(node) =0.01437372
## class counts: 12 2
## probabilities: 0.857 0.143
##
## Node number 709: 116 observations, complexity param=0.0003174603
## predicted class=N expected loss=0.2672414 P(node) =0.1190965
## class counts: 85 31
## probabilities: 0.733 0.267
## left son=1418 (27 obs) right son=1419 (89 obs)
## Primary splits:
## startprice.log < 5.393605 to the right, improve=0.4738976, (0 missing)
##
## Node number 710: 13 observations
## predicted class=N expected loss=0.2307692 P(node) =0.01334702
## class counts: 10 3
## probabilities: 0.769 0.231
##
## Node number 711: 8 observations
## predicted class=Y expected loss=0.375 P(node) =0.008213552
## class counts: 3 5
## probabilities: 0.375 0.625
##
## Node number 868: 18 observations
## predicted class=N expected loss=0.2222222 P(node) =0.01848049
## class counts: 14 4
## probabilities: 0.778 0.222
##
## Node number 869: 27 observations, complexity param=0.005555556
## predicted class=Y expected loss=0.4814815 P(node) =0.02772074
## class counts: 13 14
## probabilities: 0.481 0.519
## left son=1738 (8 obs) right son=1739 (19 obs)
## Primary splits:
## startprice.log < 5.206629 to the left, improve=1.639376, (0 missing)
##
## Node number 1418: 27 observations
## predicted class=N expected loss=0.1851852 P(node) =0.02772074
## class counts: 22 5
## probabilities: 0.815 0.185
##
## Node number 1419: 89 observations, complexity param=0.0003174603
## predicted class=N expected loss=0.2921348 P(node) =0.09137577
## class counts: 63 26
## probabilities: 0.708 0.292
## left son=2838 (80 obs) right son=2839 (9 obs)
## Primary splits:
## startprice.log < 5.347084 to the left, improve=0.4645443, (0 missing)
##
## Node number 1738: 8 observations
## predicted class=N expected loss=0.25 P(node) =0.008213552
## class counts: 6 2
## probabilities: 0.750 0.250
##
## Node number 1739: 19 observations
## predicted class=Y expected loss=0.3684211 P(node) =0.01950719
## class counts: 7 12
## probabilities: 0.368 0.632
##
## Node number 2838: 80 observations, complexity param=0.0003174603
## predicted class=N expected loss=0.275 P(node) =0.08213552
## class counts: 58 22
## probabilities: 0.725 0.275
## left son=5676 (8 obs) right son=5677 (72 obs)
## Primary splits:
## startprice.log < 5.339912 to the right, improve=0.4, (0 missing)
##
## Node number 2839: 9 observations
## predicted class=N expected loss=0.4444444 P(node) =0.009240246
## class counts: 5 4
## probabilities: 0.556 0.444
##
## Node number 5676: 8 observations
## predicted class=N expected loss=0.125 P(node) =0.008213552
## class counts: 7 1
## probabilities: 0.875 0.125
##
## Node number 5677: 72 observations, complexity param=0.0003174603
## predicted class=N expected loss=0.2916667 P(node) =0.07392197
## class counts: 51 21
## probabilities: 0.708 0.292
## left son=11354 (65 obs) right son=11355 (7 obs)
## Primary splits:
## startprice.log < 5.09056 to the right, improve=0.2906593, (0 missing)
##
## Node number 11354: 65 observations, complexity param=0.0003174603
## predicted class=N expected loss=0.2769231 P(node) =0.06673511
## class counts: 47 18
## probabilities: 0.723 0.277
## left son=22708 (14 obs) right son=22709 (51 obs)
## Primary splits:
## startprice.log < 5.192818 to the left, improve=0.6414135, (0 missing)
##
## Node number 11355: 7 observations
## predicted class=N expected loss=0.4285714 P(node) =0.007186858
## class counts: 4 3
## probabilities: 0.571 0.429
##
## Node number 22708: 14 observations
## predicted class=N expected loss=0.1428571 P(node) =0.01437372
## class counts: 12 2
## probabilities: 0.857 0.143
##
## Node number 22709: 51 observations, complexity param=0.0003174603
## predicted class=N expected loss=0.3137255 P(node) =0.0523614
## class counts: 35 16
## probabilities: 0.686 0.314
## left son=45418 (42 obs) right son=45419 (9 obs)
## Primary splits:
## startprice.log < 5.227067 to the right, improve=1.278245, (0 missing)
##
## Node number 45418: 42 observations
## predicted class=N expected loss=0.2619048 P(node) =0.04312115
## class counts: 31 11
## probabilities: 0.738 0.262
##
## Node number 45419: 9 observations
## predicted class=Y expected loss=0.4444444 P(node) =0.009240246
## class counts: 4 5
## probabilities: 0.444 0.556
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366)
## 4) startprice.log>=5.527188 276 41 N (0.85144928 0.14855072)
## 8) startprice.log>=5.972491 119 12 N (0.89915966 0.10084034) *
## 9) startprice.log< 5.972491 157 29 N (0.81528662 0.18471338)
## 18) startprice.log< 5.940158 147 24 N (0.83673469 0.16326531)
## 36) startprice.log< 5.669864 39 2 N (0.94871795 0.05128205) *
## 37) startprice.log>=5.669864 108 22 N (0.79629630 0.20370370)
## 74) startprice.log>=5.768305 67 10 N (0.85074627 0.14925373) *
## 75) startprice.log< 5.768305 41 12 N (0.70731707 0.29268293)
## 150) startprice.log< 5.720131 30 6 N (0.80000000 0.20000000) *
## 151) startprice.log>=5.720131 11 5 Y (0.45454545 0.54545455) *
## 19) startprice.log>=5.940158 10 5 N (0.50000000 0.50000000) *
## 5) startprice.log< 5.527188 248 69 N (0.72177419 0.27822581)
## 10) startprice.log< 3.365867 9 0 N (1.00000000 0.00000000) *
## 11) startprice.log>=3.365867 239 69 N (0.71129707 0.28870293)
## 22) startprice.log>=4.248424 225 60 N (0.73333333 0.26666667)
## 44) startprice.log< 5.521381 209 53 N (0.74641148 0.25358852)
## 88) startprice.log>=4.499754 185 44 N (0.76216216 0.23783784)
## 176) startprice.log< 4.828114 34 3 N (0.91176471 0.08823529) *
## 177) startprice.log>=4.828114 151 41 N (0.72847682 0.27152318)
## 354) startprice.log>=5.010602 130 33 N (0.74615385 0.25384615)
## 708) startprice.log< 5.074986 14 2 N (0.85714286 0.14285714) *
## 709) startprice.log>=5.074986 116 31 N (0.73275862 0.26724138)
## 1418) startprice.log>=5.393605 27 5 N (0.81481481 0.18518519) *
## 1419) startprice.log< 5.393605 89 26 N (0.70786517 0.29213483)
## 2838) startprice.log< 5.347084 80 22 N (0.72500000 0.27500000)
## 5676) startprice.log>=5.339912 8 1 N (0.87500000 0.12500000) *
## 5677) startprice.log< 5.339912 72 21 N (0.70833333 0.29166667)
## 11354) startprice.log>=5.09056 65 18 N (0.72307692 0.27692308)
## 22708) startprice.log< 5.192818 14 2 N (0.85714286 0.14285714) *
## 22709) startprice.log>=5.192818 51 16 N (0.68627451 0.31372549)
## 45418) startprice.log>=5.227067 42 11 N (0.73809524 0.26190476) *
## 45419) startprice.log< 5.227067 9 4 Y (0.44444444 0.55555556) *
## 11355) startprice.log< 5.09056 7 3 N (0.57142857 0.42857143) *
## 2839) startprice.log>=5.347084 9 4 N (0.55555556 0.44444444) *
## 355) startprice.log< 5.010602 21 8 N (0.61904762 0.38095238)
## 710) startprice.log< 4.961339 13 3 N (0.76923077 0.23076923) *
## 711) startprice.log>=4.961339 8 3 Y (0.37500000 0.62500000) *
## 89) startprice.log< 4.499754 24 9 N (0.62500000 0.37500000) *
## 45) startprice.log>=5.521381 16 7 N (0.56250000 0.43750000) *
## 23) startprice.log< 4.248424 14 5 Y (0.35714286 0.64285714) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.log>=4.863673 162 78 N (0.51851852 0.48148148)
## 12) startprice.log>=6.024113 27 7 N (0.74074074 0.25925926) *
## 13) startprice.log< 6.024113 135 64 Y (0.47407407 0.52592593)
## 26) startprice.log< 5.010535 10 2 N (0.80000000 0.20000000) *
## 27) startprice.log>=5.010535 125 56 Y (0.44800000 0.55200000)
## 54) startprice.log>=5.176086 98 48 Y (0.48979592 0.51020408)
## 108) startprice.log< 5.700393 67 29 N (0.56716418 0.43283582)
## 216) startprice.log>=5.549046 9 2 N (0.77777778 0.22222222) *
## 217) startprice.log< 5.549046 58 27 N (0.53448276 0.46551724)
## 434) startprice.log< 5.501177 45 18 N (0.60000000 0.40000000)
## 868) startprice.log>=5.322712 18 4 N (0.77777778 0.22222222) *
## 869) startprice.log< 5.322712 27 13 Y (0.48148148 0.51851852)
## 1738) startprice.log< 5.206629 8 2 N (0.75000000 0.25000000) *
## 1739) startprice.log>=5.206629 19 7 Y (0.36842105 0.63157895) *
## 435) startprice.log>=5.501177 13 4 Y (0.30769231 0.69230769) *
## 109) startprice.log>=5.700393 31 10 Y (0.32258065 0.67741935) *
## 55) startprice.log< 5.176086 27 8 Y (0.29629630 0.70370370) *
## 7) startprice.log< 4.863673 288 26 Y (0.09027778 0.90972222) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6631893
## 3 0.2 0.7657573
## 4 0.3 0.8034188
## 5 0.4 0.8048246
## 6 0.5 0.8022727
## 7 0.6 0.7929412
## 8 0.7 0.7346405
## 9 0.8 0.7100271
## 10 0.9 0.7100271
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 429
## 2 Y 83
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 95
## 2 367
## Prediction
## Reference N Y
## N 429 95
## Y 83 367
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.172485e-01 6.330658e-01 7.915181e-01 8.410439e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 6.422756e-75 4.096641e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6457990
## 3 0.2 0.7080745
## 4 0.3 0.7142857
## 5 0.4 0.7191539
## 6 0.5 0.7314702
## 7 0.6 0.7439490
## 8 0.7 0.7070707
## 9 0.8 0.6818874
## 10 0.9 0.6818874
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 392
## 2 Y 118
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 83
## 2 292
## Prediction
## Reference N Y
## N 392 83
## Y 118 292
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.728814e-01 5.406159e-01 7.438293e-01 8.001036e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 3.908036e-48 1.647699e-02
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart biddable, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.477 0.008
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8781425 0.4 0.8048246 0.8172485
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7915181 0.8410439 0.6330658 0.8075096
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.743949 0.7728814
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7438293 0.8001036 0.5406159if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable, startprice.log"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0133 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.51111111 0 1.0000000
## 2 0.01444444 1 0.4888889
## 3 0.01333333 3 0.4600000
##
## Variable importance
## biddable startprice.log
## 57 43
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.1499, (0 missing)
## startprice.log < 4.610145 to the right, improve=117.2126, (0 missing)
## Surrogate splits:
## startprice.log < 5.030417 to the right, agree=0.751, adj=0.46, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations, complexity param=0.01444444
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (162 obs) right son=7 (288 obs)
## Primary splits:
## startprice.log < 4.863673 to the right, improve=38.02778, (0 missing)
##
## Node number 6: 162 observations, complexity param=0.01444444
## predicted class=N expected loss=0.4814815 P(node) =0.1663244
## class counts: 84 78
## probabilities: 0.519 0.481
## left son=12 (27 obs) right son=13 (135 obs)
## Primary splits:
## startprice.log < 6.024113 to the right, improve=3.2, (0 missing)
##
## Node number 7: 288 observations
## predicted class=Y expected loss=0.09027778 P(node) =0.2956879
## class counts: 26 262
## probabilities: 0.090 0.910
##
## Node number 12: 27 observations
## predicted class=N expected loss=0.2592593 P(node) =0.02772074
## class counts: 20 7
## probabilities: 0.741 0.259
##
## Node number 13: 135 observations
## predicted class=Y expected loss=0.4740741 P(node) =0.1386037
## class counts: 64 71
## probabilities: 0.474 0.526
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.log>=4.863673 162 78 N (0.51851852 0.48148148)
## 12) startprice.log>=6.024113 27 7 N (0.74074074 0.25925926) *
## 13) startprice.log< 6.024113 135 64 Y (0.47407407 0.52592593) *
## 7) startprice.log< 4.863673 288 26 Y (0.09027778 0.90972222) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6320225
## 4 0.3 0.7628866
## 5 0.4 0.7628866
## 6 0.5 0.7628866
## 7 0.6 0.7100271
## 8 0.7 0.7100271
## 9 0.8 0.7100271
## 10 0.9 0.7100271
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 434
## 2 Y 117
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 90
## 2 333
## Prediction
## Reference N Y
## N 434 90
## Y 117 333
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.874743e-01 5.706620e-01 7.604190e-01 8.127790e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 3.596136e-59 7.074280e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6332046
## 4 0.3 0.7582697
## 5 0.4 0.7582697
## 6 0.5 0.7582697
## 7 0.6 0.6818874
## 8 0.7 0.6818874
## 9 0.8 0.6818874
## 10 0.9 0.6818874
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 397
## 2 Y 112
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 78
## 2 298
## Prediction
## Reference N Y
## N 397 78
## Y 112 298
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.853107e-01 5.658292e-01 7.567658e-01 8.119416e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.847896e-53 1.666249e-02
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart biddable, startprice.log 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.004 0.012 0.8172434
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.7628866 0.7731117
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.760419 0.812779 0.537726 0.8080205
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7582697 0.7853107
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7567658 0.8119416 0.5658292
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009511979 0.0250436# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.log"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8846 -0.7020 -0.5158 0.7747 2.1091
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.36119 0.47914 4.928 8.31e-07 ***
## biddable 1.77676 0.16636 10.681 < 2e-16 ***
## startprice.log -0.68628 0.08788 -7.809 5.75e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.6 on 973 degrees of freedom
## Residual deviance: 937.2 on 971 degrees of freedom
## AIC: 943.2
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6338028
## 3 0.2 0.7118644
## 4 0.3 0.7466391
## 5 0.4 0.7538126
## 6 0.5 0.7570621
## 7 0.6 0.7274939
## 8 0.7 0.6949384
## 9 0.8 0.5460317
## 10 0.9 0.3907638
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 424
## 2 Y 115
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 100
## 2 335
## Prediction
## Reference N Y
## N 424 100
## Y 115 335
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.792608e-01 5.549108e-01 7.518769e-01 8.049446e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 3.140215e-55 3.396829e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6361521
## 3 0.2 0.7043062
## 4 0.3 0.7508691
## 5 0.4 0.7604938
## 6 0.5 0.7597484
## 7 0.6 0.7415426
## 8 0.7 0.6798780
## 9 0.8 0.5150977
## 10 0.9 0.3913894
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 383
## 2 Y 102
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 92
## 2 308
## Prediction
## Reference N Y
## N 383 92
## Y 102 308
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.807910e-01 5.584673e-01 7.520575e-01 8.076410e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.749595e-51 5.181742e-01
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm biddable, startprice.log 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.043 0.015 0.840229
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.7570621 0.7803039
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7518769 0.8049446 0.5567446 0.8402773
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7604938 0.780791
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7520575 0.807641 0.5584673 943.1953
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0141875 0.02978891if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.log, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9798 -0.7021 -0.5105 0.7393 2.1205
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.47942 0.48789 5.082 3.74e-07
## biddable 2.51697 1.38834 1.813 0.06984
## startprice.log -0.70856 0.08961 -7.907 2.63e-15
## `biddable:D.terms.n.post.stop` 0.12864 0.27815 0.462 0.64372
## `biddable:D.TfIdf.sum.post.stem` 0.08918 0.19998 0.446 0.65564
## `biddable:D.ratio.nstopwrds.nwrds` -0.46307 1.37902 -0.336 0.73703
## `biddable:D.npnct06.log` -0.42306 0.81415 -0.520 0.60332
## `biddable:D.nchrs.log` -0.08457 0.83777 -0.101 0.91959
## `biddable:D.nwrds.unq.log` -0.81356 2.75778 -0.295 0.76799
## `biddable:cellular.fctr1` -0.11270 0.29020 -0.388 0.69776
## `biddable:cellular.fctrUnknown` -1.14917 0.36338 -3.162 0.00156
##
## (Intercept) ***
## biddable .
## startprice.log ***
## `biddable:D.terms.n.post.stop`
## `biddable:D.TfIdf.sum.post.stem`
## `biddable:D.ratio.nstopwrds.nwrds`
## `biddable:D.npnct06.log`
## `biddable:D.nchrs.log`
## `biddable:D.nwrds.unq.log`
## `biddable:cellular.fctr1`
## `biddable:cellular.fctrUnknown` **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 924.87 on 963 degrees of freedom
## AIC: 946.87
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6346968
## 3 0.2 0.7131367
## 4 0.3 0.7476636
## 5 0.4 0.7563396
## 6 0.5 0.7534247
## 7 0.6 0.7457213
## 8 0.7 0.7054054
## 9 0.8 0.5574803
## 10 0.9 0.4112478
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 410
## 2 Y 107
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 114
## 2 343
## Prediction
## Reference N Y
## N 410 114
## Y 107 343
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.731006e-01 5.440674e-01 7.454801e-01 7.990590e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 2.208903e-52 6.865042e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6371406
## 3 0.2 0.7063340
## 4 0.3 0.7508691
## 5 0.4 0.7636816
## 6 0.5 0.7496823
## 7 0.6 0.7453083
## 8 0.7 0.6913947
## 9 0.8 0.5291005
## 10 0.9 0.3906250
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 388
## 2 Y 103
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 87
## 2 307
## Prediction
## Reference N Y
## N 388 87
## Y 103 307
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.853107e-01 5.671369e-01 7.567658e-01 8.119416e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.847896e-53 2.765005e-01
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats
## 1 biddable, startprice.log, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.015 0.014
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8427523 0.4 0.7563396 0.7690028
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7454801 0.799059 0.533209 0.8394608
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7636816 0.7853107
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7567658 0.8119416 0.5671369 946.8663
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01318051 0.02966064# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4139 -0.6806 -0.2093 0.6294 2.4504
##
## Coefficients: (8 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 1.115e+01 5.554e+00
## biddable 1.756e+00 2.001e-01
## D.npnct15.log 2.102e+00 8.597e-01
## D.npnct03.log 3.702e-01 1.598e+00
## D.terms.n.stem.stop.Ratio -7.307e+00 4.931e+00
## D.ratio.sum.TfIdf.nwrds -2.625e-02 1.561e-01
## .rnorm 9.069e-03 9.219e-02
## D.npnct01.log 1.488e-01 5.351e-01
## D.TfIdf.sum.stem.stop.Ratio -6.425e-01 2.954e+00
## storage.fctr16 1.292e-01 4.468e-01
## storage.fctr32 1.068e-02 4.798e-01
## storage.fctr64 6.931e-01 4.660e-01
## storage.fctrUnknown -5.908e-02 6.393e-01
## D.npnct11.log -1.448e-01 3.497e-01
## D.npnct10.log -2.305e+01 1.342e+03
## D.npnct08.log 6.394e-01 6.570e-01
## `prdline.my.fctriPad 1` 9.438e-01 5.717e-01
## `prdline.my.fctriPad 2` 9.306e-01 5.857e-01
## `prdline.my.fctriPad 3+` 1.418e+00 5.653e-01
## prdline.my.fctriPadAir 2.141e+00 5.824e-01
## prdline.my.fctriPadmini 1.218e+00 5.489e-01
## `prdline.my.fctriPadmini 2+` 1.647e+00 5.975e-01
## color.fctrBlack 1.525e-01 2.530e-01
## color.fctrGold -4.025e-01 4.988e-01
## `color.fctrSpace Gray` -1.976e-01 3.271e-01
## color.fctrWhite -6.964e-02 2.494e-01
## D.npnct06.log -1.980e+00 1.131e+00
## D.npnct28.log -1.615e+00 1.733e+03
## D.npnct12.log 1.061e-01 7.387e-01
## D.npnct09.log -9.715e+00 8.052e+02
## D.ndgts.log 6.738e-01 4.421e-01
## cellular.fctr1 7.888e-02 2.153e-01
## cellular.fctrUnknown -2.491e-01 4.683e-01
## D.npnct14.log -1.634e+00 1.050e+00
## D.terms.n.post.stop -8.800e-02 4.845e-02
## D.npnct05.log -3.935e+00 1.811e+00
## `condition.fctrFor parts or not working` -2.958e-01 3.909e-01
## `condition.fctrManufacturer refurbished` 4.264e-01 5.999e-01
## condition.fctrNew -3.395e-01 3.014e-01
## `condition.fctrNew other (see details)` 2.402e-01 4.461e-01
## `condition.fctrSeller refurbished` -9.435e-01 4.800e-01
## idseq.my -4.371e-04 2.038e-04
## startprice.log -1.013e+00 1.451e-01
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.730e+00 7.527e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.308e+00 8.149e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 8.029e-01 7.378e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -8.805e-01 7.819e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.761e-01 6.075e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.461e+00 7.590e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.781e-01 9.298e-01
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.458e-01 1.053e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` 5.968e-01 9.082e-01
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.503e+01 7.825e+02
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -2.963e-01 6.662e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -4.547e-01 8.270e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.059e+00 8.465e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 3.953e-01 8.763e-01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.631e+00 9.431e-01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.112e+00 1.308e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.009e+00 7.694e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -4.385e-01 9.842e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.685e+00 1.416e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -5.645e-01 1.358e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## z value Pr(>|z|)
## (Intercept) 2.008 0.044695 *
## biddable 8.777 < 2e-16 ***
## D.npnct15.log 2.445 0.014505 *
## D.npnct03.log 0.232 0.816809
## D.terms.n.stem.stop.Ratio -1.482 0.138380
## D.ratio.sum.TfIdf.nwrds -0.168 0.866453
## .rnorm 0.098 0.921638
## D.npnct01.log 0.278 0.780947
## D.TfIdf.sum.stem.stop.Ratio -0.218 0.827793
## storage.fctr16 0.289 0.772468
## storage.fctr32 0.022 0.982241
## storage.fctr64 1.487 0.136923
## storage.fctrUnknown -0.092 0.926362
## D.npnct11.log -0.414 0.678791
## D.npnct10.log -0.017 0.986298
## D.npnct08.log 0.973 0.330434
## `prdline.my.fctriPad 1` 1.651 0.098767 .
## `prdline.my.fctriPad 2` 1.589 0.112103
## `prdline.my.fctriPad 3+` 2.509 0.012109 *
## prdline.my.fctriPadAir 3.676 0.000237 ***
## prdline.my.fctriPadmini 2.220 0.026447 *
## `prdline.my.fctriPadmini 2+` 2.756 0.005846 **
## color.fctrBlack 0.603 0.546642
## color.fctrGold -0.807 0.419644
## `color.fctrSpace Gray` -0.604 0.545864
## color.fctrWhite -0.279 0.780058
## D.npnct06.log -1.750 0.080057 .
## D.npnct28.log -0.001 0.999257
## D.npnct12.log 0.144 0.885800
## D.npnct09.log -0.012 0.990373
## D.ndgts.log 1.524 0.127523
## cellular.fctr1 0.366 0.714052
## cellular.fctrUnknown -0.532 0.594791
## D.npnct14.log -1.556 0.119735
## D.terms.n.post.stop -1.816 0.069326 .
## D.npnct05.log -2.173 0.029771 *
## `condition.fctrFor parts or not working` -0.757 0.449213
## `condition.fctrManufacturer refurbished` 0.711 0.477236
## condition.fctrNew -1.126 0.260016
## `condition.fctrNew other (see details)` 0.538 0.590311
## `condition.fctrSeller refurbished` -1.966 0.049326 *
## idseq.my -2.145 0.031969 *
## startprice.log -6.981 2.93e-12 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.628 0.000286 ***
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.605 0.108386
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.088 0.276463
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.126 0.260122
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.619 0.535793
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.925 0.054237 .
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.407 0.684291
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.139 0.889839
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.657 0.511076
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.019 0.984670
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.445 0.656508
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.550 0.582445
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.251 0.210790
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.451 0.651875
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.729 0.083729 .
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.851 0.394943
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.311 0.189774
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.446 0.655950
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.602 0.009269 **
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.416 0.677548
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 809.65 on 911 degrees of freedom
## AIC: 935.65
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6928515
## 3 0.2 0.7406082
## 4 0.3 0.7723577
## 5 0.4 0.7868132
## 6 0.5 0.7944573
## 7 0.6 0.7892814
## 8 0.7 0.7584416
## 9 0.8 0.6550725
## 10 0.9 0.4916944
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 452
## 2 Y 106
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 72
## 2 344
## Prediction
## Reference N Y
## N 452 72
## Y 106 344
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.172485e-01 6.304031e-01 7.915181e-01 8.410439e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 6.422756e-75 1.338138e-02
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6713287
## 3 0.2 0.7119675
## 4 0.3 0.7451429
## 5 0.4 0.7518428
## 6 0.5 0.7580026
## 7 0.6 0.7503374
## 8 0.7 0.7296137
## 9 0.8 0.6402536
## 10 0.9 0.4808743
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 400
## 2 Y 114
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 75
## 2 296
## Prediction
## Reference N Y
## N 400 75
## Y 114 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.864407e-01 5.677516e-01 7.579436e-01 8.130160e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.826387e-54 5.708122e-03
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.427 0.159
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8851187 0.5 0.7944573 0.7618107
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7915181 0.8410439 0.5194818 0.8296945
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7580026 0.7864407
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7579436 0.813016 0.5677516 935.6464
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009749 0.02418586rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 86.042 111.324 25.282
## 11 fit.models 7 1 111.325 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 115.318 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
#stop(here")
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 115.318 115.336 0.019
## 2 fit.models_1_glm 2 0 115.337 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.5506 -0.6515 -0.1608 0.5949 2.4567
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 6.178e+01 4.292e+01
## biddable 1.803e+00 2.068e-01
## D.ratio.nstopwrds.nwrds -1.748e+01 7.390e+00
## D.npnct15.log 1.527e+00 9.535e-01
## D.npnct03.log 1.018e+00 1.983e+00
## D.terms.n.stem.stop.Ratio -3.393e+01 3.919e+01
## D.ratio.sum.TfIdf.nwrds -8.105e-02 6.772e-01
## .rnorm 4.380e-03 9.445e-02
## D.npnct01.log 9.274e-02 6.268e-01
## D.TfIdf.sum.stem.stop.Ratio -7.012e+00 1.819e+01
## storage.fctr16 5.772e-02 4.535e-01
## storage.fctr32 -9.689e-02 4.907e-01
## storage.fctr64 6.485e-01 4.754e-01
## storage.fctrUnknown -1.633e-01 6.486e-01
## D.npnct11.log -1.090e-01 3.939e-01
## D.npnct10.log -2.504e+01 1.217e+03
## D.TfIdf.sum.post.stop -8.331e-01 2.685e+00
## D.TfIdf.sum.post.stem 1.085e+00 2.792e+00
## D.sum.TfIdf NA NA
## D.npnct13.log 1.072e-01 4.195e-01
## D.npnct08.log 8.867e-01 7.267e-01
## `prdline.my.fctriPad 1` 1.003e+00 5.816e-01
## `prdline.my.fctriPad 2` 8.797e-01 5.954e-01
## `prdline.my.fctriPad 3+` 1.323e+00 5.750e-01
## prdline.my.fctriPadAir 2.161e+00 5.921e-01
## prdline.my.fctriPadmini 1.223e+00 5.593e-01
## `prdline.my.fctriPadmini 2+` 1.659e+00 6.059e-01
## color.fctrBlack 1.822e-01 2.608e-01
## color.fctrGold -4.855e-01 5.392e-01
## `color.fctrSpace Gray` -1.409e-01 3.368e-01
## color.fctrWhite 7.647e-03 2.599e-01
## D.npnct16.log 2.426e+00 1.905e+00
## D.npnct24.log -4.795e+00 5.913e+00
## D.nstopwrds.log 4.484e+00 2.124e+00
## D.npnct06.log -4.935e+00 2.309e+00
## D.npnct28.log -2.610e+00 1.743e+03
## D.nuppr.log 2.132e+00 3.396e+00
## D.nchrs.log -3.885e+00 4.031e+00
## D.npnct12.log 7.763e-01 8.166e-01
## D.nwrds.log 8.015e-01 3.335e+00
## D.npnct09.log -8.727e+00 8.198e+02
## D.ndgts.log 4.222e-01 5.733e-01
## D.nwrds.unq.log -4.625e+00 4.029e+00
## `carrier.fctrAT&T` 7.071e-02 7.002e-01
## carrier.fctrOther 1.604e+01 1.597e+03
## carrier.fctrSprint 8.125e-01 9.375e-01
## `carrier.fctrT-Mobile` -1.070e+00 1.181e+00
## carrier.fctrUnknown -2.282e-01 4.828e-01
## carrier.fctrVerizon 3.978e-01 7.522e-01
## cellular.fctr1 8.486e-04 6.363e-01
## cellular.fctrUnknown NA NA
## D.npnct14.log -2.588e+00 1.307e+00
## D.terms.n.post.stem 3.454e+00 4.287e+00
## D.terms.n.post.stop -3.537e+00 4.253e+00
## D.npnct05.log -3.405e+00 1.972e+00
## `condition.fctrFor parts or not working` -4.507e-01 4.100e-01
## `condition.fctrManufacturer refurbished` 3.981e-01 6.139e-01
## condition.fctrNew -2.824e-01 3.107e-01
## `condition.fctrNew other (see details)` 3.039e-02 4.695e-01
## `condition.fctrSeller refurbished` -1.007e+00 4.921e-01
## idseq.my -4.443e-04 2.092e-04
## startprice.log -1.043e+00 1.469e-01
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.493e+00 8.274e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.021e+00 8.908e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 7.596e-01 8.145e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.330e+00 9.275e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -4.528e-01 6.947e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.137e+00 8.261e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.197e-03 1.020e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` 3.851e-02 1.185e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` 3.653e-01 9.627e-01
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.494e+01 7.547e+02
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -8.806e-02 7.145e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -6.875e-01 1.021e+00
## `prdline.my.fctriPadmini:.clusterid.fctr3` 7.195e-01 9.506e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 4.675e-01 9.433e-01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.730e+00 1.006e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.334e+00 1.498e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.106e+00 8.923e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -6.931e-01 1.021e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 3.411e+00 1.499e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -7.792e-01 1.434e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## z value Pr(>|z|)
## (Intercept) 1.440 0.149997
## biddable 8.719 < 2e-16 ***
## D.ratio.nstopwrds.nwrds -2.366 0.017997 *
## D.npnct15.log 1.602 0.109228
## D.npnct03.log 0.514 0.607460
## D.terms.n.stem.stop.Ratio -0.866 0.386575
## D.ratio.sum.TfIdf.nwrds -0.120 0.904739
## .rnorm 0.046 0.963012
## D.npnct01.log 0.148 0.882380
## D.TfIdf.sum.stem.stop.Ratio -0.386 0.699829
## storage.fctr16 0.127 0.898716
## storage.fctr32 -0.197 0.843465
## storage.fctr64 1.364 0.172548
## storage.fctrUnknown -0.252 0.801171
## D.npnct11.log -0.277 0.781974
## D.npnct10.log -0.021 0.983588
## D.TfIdf.sum.post.stop -0.310 0.756374
## D.TfIdf.sum.post.stem 0.388 0.697647
## D.sum.TfIdf NA NA
## D.npnct13.log 0.256 0.798247
## D.npnct08.log 1.220 0.222425
## `prdline.my.fctriPad 1` 1.725 0.084513 .
## `prdline.my.fctriPad 2` 1.478 0.139522
## `prdline.my.fctriPad 3+` 2.301 0.021395 *
## prdline.my.fctriPadAir 3.650 0.000263 ***
## prdline.my.fctriPadmini 2.187 0.028717 *
## `prdline.my.fctriPadmini 2+` 2.738 0.006183 **
## color.fctrBlack 0.698 0.484880
## color.fctrGold -0.900 0.367892
## `color.fctrSpace Gray` -0.419 0.675559
## color.fctrWhite 0.029 0.976525
## D.npnct16.log 1.273 0.202928
## D.npnct24.log -0.811 0.417459
## D.nstopwrds.log 2.111 0.034766 *
## D.npnct06.log -2.137 0.032586 *
## D.npnct28.log -0.001 0.998805
## D.nuppr.log 0.628 0.530079
## D.nchrs.log -0.964 0.335133
## D.npnct12.log 0.951 0.341759
## D.nwrds.log 0.240 0.810089
## D.npnct09.log -0.011 0.991507
## D.ndgts.log 0.736 0.461475
## D.nwrds.unq.log -1.148 0.251006
## `carrier.fctrAT&T` 0.101 0.919566
## carrier.fctrOther 0.010 0.991987
## carrier.fctrSprint 0.867 0.386129
## `carrier.fctrT-Mobile` -0.906 0.365113
## carrier.fctrUnknown -0.473 0.636368
## carrier.fctrVerizon 0.529 0.596945
## cellular.fctr1 0.001 0.998936
## cellular.fctrUnknown NA NA
## D.npnct14.log -1.980 0.047667 *
## D.terms.n.post.stem 0.806 0.420422
## D.terms.n.post.stop -0.832 0.405657
## D.npnct05.log -1.726 0.084297 .
## `condition.fctrFor parts or not working` -1.099 0.271609
## `condition.fctrManufacturer refurbished` 0.648 0.516718
## condition.fctrNew -0.909 0.363414
## `condition.fctrNew other (see details)` 0.065 0.948384
## `condition.fctrSeller refurbished` -2.047 0.040654 *
## idseq.my -2.124 0.033704 *
## startprice.log -7.102 1.23e-12 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 3.013 0.002589 **
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.146 0.251773
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.933 0.351023
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.434 0.151523
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.652 0.514553
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.376 0.168767
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.001 0.999064
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.033 0.974073
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.379 0.704344
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.020 0.984208
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.123 0.901910
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.674 0.500516
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.757 0.449106
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.496 0.620184
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.719 0.085537 .
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.891 0.373039
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.240 0.215094
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.679 0.497248
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.276 0.022866 *
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.543 0.586788
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 784.26 on 894 degrees of freedom
## AIC: 944.26
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7042030
## 3 0.2 0.7572464
## 4 0.3 0.7795918
## 5 0.4 0.8000000
## 6 0.5 0.8004587
## 7 0.6 0.7912621
## 8 0.7 0.7587097
## 9 0.8 0.6676218
## 10 0.9 0.5065789
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 451 73
## 2 Y 101 349
## Prediction
## Reference N Y
## N 451 73
## Y 101 349
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.213552e-01 6.390503e-01 7.958255e-01 8.449246e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 2.791948e-77 4.067137e-02
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6672630
## 3 0.2 0.7081244
## 4 0.3 0.7317621
## 5 0.4 0.7475728
## 6 0.5 0.7573062
## 7 0.6 0.7576975
## 8 0.7 0.7308782
## 9 0.8 0.6448598
## 10 0.9 0.4990958
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 421 54
## 2 Y 127 283
## Prediction
## Reference N Y
## N 421 54
## Y 127 283
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.954802e-01 5.836701e-01 7.673772e-01 8.215999e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 4.462035e-58 8.712317e-08
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.453 0.221
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8949364 0.5 0.8004587 0.7515416
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7958255 0.8449246 0.4983565 0.8269987
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7576975 0.7954802
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7673772 0.8215999 0.5836701 944.2624
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009327212 0.01557657
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 115.337 120.613 5.277
## 3 fit.models_1_bayesglm 3 0 120.614 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loading required package: lme4
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4001 -0.6734 -0.2284 0.6303 2.3665
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 10.2842486 6.4893782
## biddable 1.7573338 0.1973230
## D.ratio.nstopwrds.nwrds -1.6600153 2.3190873
## D.npnct15.log 1.7243105 0.9036160
## D.npnct03.log 0.1709185 1.6482521
## D.terms.n.stem.stop.Ratio -5.3365408 5.2987561
## D.ratio.sum.TfIdf.nwrds -0.2975649 0.2792717
## .rnorm 0.0085918 0.0914511
## D.npnct01.log 0.0976625 0.5307216
## D.TfIdf.sum.stem.stop.Ratio 0.0820975 3.4101394
## storage.fctr16 -0.0312320 0.4034695
## storage.fctr32 -0.1607385 0.4366170
## storage.fctr64 0.5414545 0.4252873
## storage.fctrUnknown -0.1556108 0.5766591
## D.npnct11.log -0.0703486 0.3534029
## D.npnct10.log -7.4868921 7.5862091
## D.TfIdf.sum.post.stop 0.0536202 0.2889917
## D.TfIdf.sum.post.stem 0.0772985 0.3046543
## D.sum.TfIdf 0.0772985 0.3046543
## D.npnct13.log 0.0038540 0.3520800
## D.npnct08.log 0.6436918 0.6721688
## `prdline.my.fctriPad 1` 0.6129401 0.4905833
## `prdline.my.fctriPad 2` 0.5590282 0.4989079
## `prdline.my.fctriPad 3+` 0.9228015 0.4730864
## prdline.my.fctriPadAir 1.6331927 0.4867491
## prdline.my.fctriPadmini 0.8234679 0.4623652
## `prdline.my.fctriPadmini 2+` 1.1789282 0.5051392
## color.fctrBlack 0.1754209 0.2484243
## color.fctrGold -0.4232961 0.4938666
## `color.fctrSpace Gray` -0.1515756 0.3206617
## color.fctrWhite -0.0476805 0.2450863
## D.npnct16.log 2.0953668 1.6045127
## D.npnct24.log 0.0096372 2.3183564
## D.nstopwrds.log 0.7430813 0.6531890
## D.npnct06.log -3.9327506 1.9017435
## D.npnct28.log -0.0586218 2.1808018
## D.nuppr.log -0.0921862 0.4809059
## D.nchrs.log -0.1789244 0.4884053
## D.npnct12.log 0.5005917 0.7367023
## D.nwrds.log -0.0174159 0.7577466
## D.npnct09.log -1.8762155 4.8176276
## D.ndgts.log 0.6118216 0.4309216
## D.nwrds.unq.log -0.3662339 0.9899972
## `carrier.fctrAT&T` -0.0925699 0.7617184
## carrier.fctrOther 1.4827438 1.6840761
## carrier.fctrSprint 0.5934538 0.9178047
## `carrier.fctrT-Mobile` -1.0346543 1.0426395
## carrier.fctrUnknown -0.4669640 0.7673418
## carrier.fctrVerizon 0.2125612 0.7888289
## cellular.fctr1 0.1663025 0.7310228
## cellular.fctrUnknown 0.1023197 0.8486886
## D.npnct14.log -1.9746863 1.0751784
## D.terms.n.post.stem -0.1008951 0.1999550
## D.terms.n.post.stop -0.1183221 0.1987110
## D.npnct05.log -2.9551434 1.4986226
## `condition.fctrFor parts or not working` -0.3343721 0.3843888
## `condition.fctrManufacturer refurbished` 0.3922178 0.5731989
## condition.fctrNew -0.2738450 0.2966734
## `condition.fctrNew other (see details)` 0.0766691 0.4358370
## `condition.fctrSeller refurbished` -0.8332128 0.4550953
## idseq.my -0.0004650 0.0002007
## startprice.log -0.9436826 0.1330383
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.9712145 0.6807140
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.8582323 0.7384926
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.6062147 0.6726430
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.0755689 0.7438557
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.3754393 0.5611276
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.8918425 0.7006279
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.0683592 0.8317439
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.3231263 0.9057692
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.2816765 0.7876395
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.8031878 1.5583563
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.3338476 0.6084017
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.6236151 0.8101234
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.7536847 0.7745361
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.4381060 0.7847287
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.6472311 0.8451679
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.7940189 1.1468944
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.8093186 0.7297984
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.5468567 0.8348141
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.7465581 1.1464691
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.7231388 1.1060445
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.0000000 2.5000000
## z value Pr(>|z|)
## (Intercept) 1.585 0.113016
## biddable 8.906 < 2e-16 ***
## D.ratio.nstopwrds.nwrds -0.716 0.474112
## D.npnct15.log 1.908 0.056361 .
## D.npnct03.log 0.104 0.917410
## D.terms.n.stem.stop.Ratio -1.007 0.313872
## D.ratio.sum.TfIdf.nwrds -1.066 0.286648
## .rnorm 0.094 0.925149
## D.npnct01.log 0.184 0.853999
## D.TfIdf.sum.stem.stop.Ratio 0.024 0.980793
## storage.fctr16 -0.077 0.938298
## storage.fctr32 -0.368 0.712765
## storage.fctr64 1.273 0.202965
## storage.fctrUnknown -0.270 0.787276
## D.npnct11.log -0.199 0.842215
## D.npnct10.log -0.987 0.323688
## D.TfIdf.sum.post.stop 0.186 0.852804
## D.TfIdf.sum.post.stem 0.254 0.799708
## D.sum.TfIdf 0.254 0.799708
## D.npnct13.log 0.011 0.991266
## D.npnct08.log 0.958 0.338247
## `prdline.my.fctriPad 1` 1.249 0.211515
## `prdline.my.fctriPad 2` 1.121 0.262499
## `prdline.my.fctriPad 3+` 1.951 0.051105 .
## prdline.my.fctriPadAir 3.355 0.000793 ***
## prdline.my.fctriPadmini 1.781 0.074914 .
## `prdline.my.fctriPadmini 2+` 2.334 0.019603 *
## color.fctrBlack 0.706 0.480105
## color.fctrGold -0.857 0.391386
## `color.fctrSpace Gray` -0.473 0.636430
## color.fctrWhite -0.195 0.845749
## D.npnct16.log 1.306 0.191579
## D.npnct24.log 0.004 0.996683
## D.nstopwrds.log 1.138 0.255279
## D.npnct06.log -2.068 0.038643 *
## D.npnct28.log -0.027 0.978555
## D.nuppr.log -0.192 0.847983
## D.nchrs.log -0.366 0.714108
## D.npnct12.log 0.680 0.496819
## D.nwrds.log -0.023 0.981663
## D.npnct09.log -0.389 0.696945
## D.ndgts.log 1.420 0.155666
## D.nwrds.unq.log -0.370 0.711431
## `carrier.fctrAT&T` -0.122 0.903273
## carrier.fctrOther 0.880 0.378616
## carrier.fctrSprint 0.647 0.517890
## `carrier.fctrT-Mobile` -0.992 0.321031
## carrier.fctrUnknown -0.609 0.542824
## carrier.fctrVerizon 0.269 0.787572
## cellular.fctr1 0.227 0.820041
## cellular.fctrUnknown 0.121 0.904038
## D.npnct14.log -1.837 0.066267 .
## D.terms.n.post.stem -0.505 0.613847
## D.terms.n.post.stop -0.595 0.551544
## D.npnct05.log -1.972 0.048620 *
## `condition.fctrFor parts or not working` -0.870 0.384366
## `condition.fctrManufacturer refurbished` 0.684 0.493810
## condition.fctrNew -0.923 0.355980
## `condition.fctrNew other (see details)` 0.176 0.860363
## `condition.fctrSeller refurbished` -1.831 0.067122 .
## idseq.my -2.317 0.020502 *
## startprice.log -7.093 1.31e-12 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.896 0.003782 **
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.162 0.245178
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.901 0.367459
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.446 0.148195
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.669 0.503444
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.273 0.203047
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.082 0.934497
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.357 0.721285
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.358 0.720627
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.157 0.247228
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.549 0.583192
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.770 0.441432
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.973 0.330514
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.558 0.576647
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.949 0.051296 .
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.692 0.488736
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.109 0.267447
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.655 0.512427
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.396 0.016590 *
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.654 0.513237
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.6 on 973 degrees of freedom
## Residual deviance: 794.6 on 884 degrees of freedom
## AIC: 974.6
##
## Number of Fisher Scoring iterations: 16
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6958561
## 3 0.2 0.7479821
## 4 0.3 0.7785642
## 5 0.4 0.7956284
## 6 0.5 0.8004587
## 7 0.6 0.7902439
## 8 0.7 0.7516426
## 9 0.8 0.6402349
## 10 0.9 0.4824121
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 451
## 2 Y 101
## sold.fctr.predict.All.X.bayesglm.Y
## 1 73
## 2 349
## Prediction
## Reference N Y
## N 451 73
## Y 101 349
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.213552e-01 6.390503e-01 7.958255e-01 8.449246e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 2.791948e-77 4.067137e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6695652
## 3 0.2 0.7065868
## 4 0.3 0.7337808
## 5 0.4 0.7429963
## 6 0.5 0.7593308
## 7 0.6 0.7630522
## 8 0.7 0.7249284
## 9 0.8 0.6410256
## 10 0.9 0.4601113
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 423
## 2 Y 125
## sold.fctr.predict.All.X.bayesglm.Y
## 1 52
## 2 285
## Prediction
## Reference N Y
## N 423 52
## Y 125 285
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.000000e-01 5.928708e-01 7.721016e-01 8.258843e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 3.310805e-60 6.237644e-08
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.635 0.362
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8908779 0.5 0.8004587 0.7669516
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7958255 0.8449246 0.5295107 0.8355584
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7630522 0.8
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7721016 0.8258843 0.5928708 974.603
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01212752 0.02326004
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_bayesglm 3 0 120.614 126.488 5.874
## 4 fit.models_1_glmnet 4 0 126.489 NA NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr"## Loading required package: glmnet
## Loaded glmnet 2.0-2
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0544 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 8900 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 89 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.9753355301
## biddable
## 1.4186035520
## D.npnct15.log
## 0.8833841816
## D.npnct03.log
## 0.0893533503
## D.terms.n.stem.stop.Ratio
## -0.5838557245
## storage.fctr64
## 0.2428437861
## storage.fctrUnknown
## -0.0280019846
## D.npnct10.log
## -1.1692524418
## D.TfIdf.sum.post.stop
## 0.0010344029
## D.TfIdf.sum.post.stem
## 0.0018434842
## D.sum.TfIdf
## 0.0019102640
## prdline.my.fctriPad 1
## 0.0871098970
## prdline.my.fctriPadAir
## 0.0740732373
## color.fctrBlack
## 0.0963310660
## color.fctrGold
## -0.2353652205
## color.fctrWhite
## -0.0403710740
## D.npnct06.log
## -0.4367253776
## D.npnct09.log
## -0.2765827232
## carrier.fctrOther
## 1.2100208205
## carrier.fctrSprint
## 0.2709322068
## carrier.fctrT-Mobile
## -0.4551417968
## carrier.fctrUnknown
## -0.3181932496
## carrier.fctrVerizon
## 0.0848262090
## cellular.fctrUnknown
## -0.1318723558
## D.npnct14.log
## -0.6804478464
## D.terms.n.post.stem
## -0.0119275180
## D.terms.n.post.stop
## -0.0109172854
## D.npnct05.log
## -1.2291373453
## condition.fctrFor parts or not working
## 0.0457977249
## condition.fctrNew
## -0.2593969773
## condition.fctrSeller refurbished
## -0.2213814714
## idseq.my
## -0.0003644435
## startprice.log
## -0.3652449952
## prdline.my.fctrUnknown:.clusterid.fctr2
## 0.5169750662
## prdline.my.fctriPad 1:.clusterid.fctr2
## 0.5066809982
## prdline.my.fctriPad 2:.clusterid.fctr2
## 0.0986256212
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -0.4630167292
## prdline.my.fctriPadAir:.clusterid.fctr2
## -0.0204172384
## prdline.my.fctriPadmini:.clusterid.fctr2
## 0.3470599044
## prdline.my.fctrUnknown:.clusterid.fctr3
## -0.4775161075
## prdline.my.fctriPad 2:.clusterid.fctr3
## -1.1493044226
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -0.2052321581
## prdline.my.fctriPadAir:.clusterid.fctr3
## -0.2202881294
## prdline.my.fctriPadmini:.clusterid.fctr3
## 0.2919294663
## prdline.my.fctriPad 1:.clusterid.fctr4
## -0.8540524090
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 0.1690271726
## prdline.my.fctriPadmini:.clusterid.fctr4
## -0.1873109080
## prdline.my.fctriPad 2:.clusterid.fctr5
## 1.5723229494
## prdline.my.fctriPadmini:.clusterid.fctr5
## -0.4799131089
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 13.0615980462
## biddable
## 1.7984103365
## D.ratio.nstopwrds.nwrds
## -3.8248222824
## D.npnct15.log
## 1.7741116999
## D.npnct03.log
## 0.0951112633
## D.terms.n.stem.stop.Ratio
## -5.8078718391
## D.ratio.sum.TfIdf.nwrds
## -0.3507968770
## .rnorm
## 0.0065016394
## D.npnct01.log
## 0.0249232418
## D.TfIdf.sum.stem.stop.Ratio
## -0.1294975229
## storage.fctr16
## -0.0435819968
## storage.fctr32
## -0.1786652751
## storage.fctr64
## 0.5511926582
## storage.fctrUnknown
## -0.1875552339
## D.npnct11.log
## -0.0991024121
## D.npnct10.log
## -9.5282186238
## D.TfIdf.sum.post.stop
## 0.0321699706
## D.TfIdf.sum.post.stem
## 0.1025316927
## D.sum.TfIdf
## 0.0966686102
## D.npnct13.log
## 0.0214655918
## D.npnct08.log
## 0.6750349310
## prdline.my.fctriPad 1
## 0.9034148359
## prdline.my.fctriPad 2
## 0.8136058945
## prdline.my.fctriPad 3+
## 1.2491915621
## prdline.my.fctriPadAir
## 2.0214797494
## prdline.my.fctriPadmini
## 1.1306267184
## prdline.my.fctriPadmini 2+
## 1.5340693440
## color.fctrBlack
## 0.1867262126
## color.fctrGold
## -0.5345521227
## color.fctrSpace Gray
## -0.1816783222
## color.fctrWhite
## -0.0557147994
## D.npnct16.log
## 2.3328929191
## D.npnct24.log
## -0.4282994305
## D.nstopwrds.log
## 1.3353717770
## D.npnct06.log
## -4.3224674321
## D.npnct28.log
## -0.9513567325
## D.nuppr.log
## -0.1801112694
## D.nchrs.log
## -0.4055057056
## D.npnct12.log
## 0.5990195386
## D.nwrds.log
## -0.0025018491
## D.npnct09.log
## -2.1529811598
## D.ndgts.log
## 0.6756134194
## D.nwrds.unq.log
## -0.7258229248
## carrier.fctrAT&T
## 0.0841989394
## carrier.fctrOther
## 6.1961336462
## carrier.fctrSprint
## 0.8380084271
## carrier.fctrT-Mobile
## -1.1270136844
## carrier.fctrUnknown
## -0.2905420116
## carrier.fctrVerizon
## 0.3867957555
## cellular.fctrUnknown
## 0.0314320802
## D.npnct14.log
## -2.1699943926
## D.terms.n.post.stem
## -0.0984701623
## D.terms.n.post.stop
## -0.1362647628
## D.npnct05.log
## -3.5965727808
## condition.fctrFor parts or not working
## -0.4007114963
## condition.fctrManufacturer refurbished
## 0.4381686537
## condition.fctrNew
## -0.2678093649
## condition.fctrNew other (see details)
## 0.0699778209
## condition.fctrSeller refurbished
## -0.9328038123
## idseq.my
## -0.0004517569
## startprice.log
## -0.9920755893
## prdline.my.fctrUnknown:.clusterid.fctr2
## 2.3814999068
## prdline.my.fctriPad 1:.clusterid.fctr2
## 1.0456498213
## prdline.my.fctriPad 2:.clusterid.fctr2
## 0.8044137479
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -1.1361599926
## prdline.my.fctriPadAir:.clusterid.fctr2
## -0.3307491501
## prdline.my.fctriPadmini:.clusterid.fctr2
## 1.1267871434
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 0.0053621728
## prdline.my.fctrUnknown:.clusterid.fctr3
## -0.0355033666
## prdline.my.fctriPad 1:.clusterid.fctr3
## 0.3979133332
## prdline.my.fctriPad 2:.clusterid.fctr3
## -5.1691175978
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -0.2734142333
## prdline.my.fctriPadAir:.clusterid.fctr3
## -0.7578308238
## prdline.my.fctriPadmini:.clusterid.fctr3
## 0.9344198655
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## 0.6446447969
## prdline.my.fctriPad 1:.clusterid.fctr4
## -1.7920829424
## prdline.my.fctriPad 2:.clusterid.fctr4
## 1.2955088952
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 1.0722662339
## prdline.my.fctriPadmini:.clusterid.fctr4
## -0.6092010590
## prdline.my.fctriPad 2:.clusterid.fctr5
## 3.4696099313
## prdline.my.fctriPadmini:.clusterid.fctr5
## -0.8395062805
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6442377
## 3 0.2 0.7052117
## 4 0.3 0.7470817
## 5 0.4 0.7771798
## 6 0.5 0.7773933
## 7 0.6 0.7723270
## 8 0.7 0.6685796
## 9 0.8 0.4670051
## 10 0.9 0.2786260
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 444
## 2 Y 113
## sold.fctr.predict.All.X.glmnet.Y
## 1 80
## 2 337
## Prediction
## Reference N Y
## N 444 80
## Y 113 337
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.018480e-01 5.993205e-01 7.754050e-01 8.264517e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.767324e-66 2.125576e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6409449
## 3 0.2 0.6810422
## 4 0.3 0.7351695
## 5 0.4 0.7560386
## 6 0.5 0.7564103
## 7 0.6 0.7397260
## 8 0.7 0.6488189
## 9 0.8 0.4288425
## 10 0.9 0.2579281
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 400
## 2 Y 115
## sold.fctr.predict.All.X.glmnet.Y
## 1 75
## 2 295
## Prediction
## Reference N Y
## N 400 75
## Y 115 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.853107e-01 5.653916e-01 7.567658e-01 8.119416e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.847896e-53 4.664158e-03
## model_id model_method
## 1 All.X.glmnet glmnet
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 5.319 0.579
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.874207 0.5 0.7773933 0.7782494
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.775405 0.8264517 0.5520479 0.8374685
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7564103 0.7853107
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7567658 0.8119416 0.5653916
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01304713 0.02731799
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_glmnet 4 0 126.489 135.776 9.287
## 5 fit.models_1_rpart 5 0 135.776 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0111 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.51111111 0 1.0000000
## 2 0.04333333 1 0.4888889
## 3 0.01111111 3 0.4022222
##
## Variable importance
## biddable
## 41
## startprice.log
## 30
## idseq.my
## 13
## condition.fctrFor parts or not working
## 2
## condition.fctrNew
## 2
## prdline.my.fctriPadAir
## 2
## prdline.my.fctriPad 1
## 2
## prdline.my.fctriPad 2
## 1
## color.fctrSpace Gray
## 1
## prdline.my.fctriPadmini 2+
## 1
## D.ratio.nstopwrds.nwrds
## 1
## D.ratio.sum.TfIdf.nwrds
## 1
## D.TfIdf.sum.post.stem
## 1
## D.TfIdf.sum.post.stop
## 1
## D.sum.TfIdf
## 1
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.149900, (0 missing)
## startprice.log < 4.610145 to the right, improve=117.212600, (0 missing)
## idseq.my < 905.5 to the right, improve= 38.912990, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 10.867390, (0 missing)
## D.nwrds.unq.log < 2.138333 to the right, improve= 8.394783, (0 missing)
## Surrogate splits:
## startprice.log < 5.030417 to the right, agree=0.751, adj=0.460, (0 split)
## idseq.my < 869 to the right, agree=0.636, adj=0.211, (0 split)
## condition.fctrFor parts or not working < 0.5 to the left, agree=0.563, adj=0.053, (0 split)
## prdline.my.fctriPad 1 < 0.5 to the left, agree=0.559, adj=0.044, (0 split)
## prdline.my.fctriPad 2 < 0.5 to the left, agree=0.553, adj=0.033, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations, complexity param=0.04333333
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (162 obs) right son=7 (288 obs)
## Primary splits:
## startprice.log < 4.863673 to the right, improve=38.027780, (0 missing)
## idseq.my < 670.5 to the right, improve=15.110620, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 3.467222, (0 missing)
## carrier.fctrUnknown < 0.5 to the right, improve= 3.372762, (0 missing)
## cellular.fctrUnknown < 0.5 to the right, improve= 2.987781, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir < 0.5 to the right, agree=0.707, adj=0.185, (0 split)
## condition.fctrNew < 0.5 to the right, agree=0.707, adj=0.185, (0 split)
## prdline.my.fctriPadmini 2+ < 0.5 to the right, agree=0.676, adj=0.099, (0 split)
## color.fctrSpace Gray < 0.5 to the right, agree=0.676, adj=0.099, (0 split)
## color.fctrGold < 0.5 to the right, agree=0.656, adj=0.043, (0 split)
##
## Node number 6: 162 observations, complexity param=0.04333333
## predicted class=N expected loss=0.4814815 P(node) =0.1663244
## class counts: 84 78
## probabilities: 0.519 0.481
## left son=12 (69 obs) right son=13 (93 obs)
## Primary splits:
## idseq.my < 906.5 to the right, improve=16.765470, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve= 5.555556, (0 missing)
## D.ratio.sum.TfIdf.nwrds < 0.3676509 to the right, improve= 5.216095, (0 missing)
## D.ratio.nstopwrds.nwrds < 0.4 to the left, improve= 4.204106, (0 missing)
## D.TfIdf.sum.stem.stop.Ratio < 0.9255282 to the left, improve= 3.901876, (0 missing)
## Surrogate splits:
## D.ratio.nstopwrds.nwrds < 0.4292763 to the left, agree=0.648, adj=0.174, (0 split)
## D.ratio.sum.TfIdf.nwrds < 0.2689021 to the right, agree=0.642, adj=0.159, (0 split)
## D.TfIdf.sum.post.stop < 1.734564 to the right, agree=0.636, adj=0.145, (0 split)
## D.TfIdf.sum.post.stem < 1.711335 to the right, agree=0.636, adj=0.145, (0 split)
## D.sum.TfIdf < 1.711335 to the right, agree=0.636, adj=0.145, (0 split)
##
## Node number 7: 288 observations
## predicted class=Y expected loss=0.09027778 P(node) =0.2956879
## class counts: 26 262
## probabilities: 0.090 0.910
##
## Node number 12: 69 observations
## predicted class=N expected loss=0.2173913 P(node) =0.07084189
## class counts: 54 15
## probabilities: 0.783 0.217
##
## Node number 13: 93 observations
## predicted class=Y expected loss=0.3225806 P(node) =0.09548255
## class counts: 30 63
## probabilities: 0.323 0.677
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.log>=4.863673 162 78 N (0.51851852 0.48148148)
## 12) idseq.my>=906.5 69 15 N (0.78260870 0.21739130) *
## 13) idseq.my< 906.5 93 30 Y (0.32258065 0.67741935) *
## 7) startprice.log< 4.863673 288 26 Y (0.09027778 0.90972222) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6320225
## 4 0.3 0.7821901
## 5 0.4 0.7821901
## 6 0.5 0.7821901
## 7 0.6 0.7821901
## 8 0.7 0.7100271
## 9 0.8 0.7100271
## 10 0.9 0.7100271
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 468
## 2 Y 125
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 56
## 2 325
## Prediction
## Reference N Y
## N 468 56
## Y 125 325
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.141684e-01 6.220873e-01 7.882906e-01 8.381305e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 3.530340e-73 4.317455e-07
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6332046
## 4 0.3 0.7600000
## 5 0.4 0.7600000
## 6 0.5 0.7600000
## 7 0.6 0.7600000
## 8 0.7 0.6818874
## 9 0.8 0.6818874
## 10 0.9 0.6818874
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 420
## 2 Y 125
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 55
## 2 285
## Prediction
## Reference N Y
## N 420 55
## Y 125 285
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.966102e-01 5.861800e-01 7.685578e-01 8.226715e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.323328e-58 2.704485e-07
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.562 0.062
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8214419 0.6 0.7821901 0.8069991
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7882906 0.8381305 0.6066762 0.8103723
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.76 0.7966102
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7685578 0.8226715 0.58618
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0166685 0.03448591
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rpart 5 0 135.776 140.938 5.163
## 6 fit.models_1_rf 6 0 140.939 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 45 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 974 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1948 matrix numeric
## oob.times 974 -none- numeric
## classes 2 -none- character
## importance 88 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 974 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 88 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.8166969
## 3 0.2 0.9404389
## 4 0.3 0.9836066
## 5 0.4 0.9966777
## 6 0.5 1.0000000
## 7 0.6 0.9988877
## 8 0.7 0.9498250
## 9 0.8 0.8578680
## 10 0.9 0.7482615
## 11 1.0 0.1132075
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 524
## 2 Y NA
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 NA
## 2 450
## Prediction
## Reference N Y
## N 524 0
## Y 0 450
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 9.962198e-01 1.000000e+00 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.919016e-263 NaN
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.63320463
## 2 0.1 0.66495287
## 3 0.2 0.69902913
## 4 0.3 0.74505495
## 5 0.4 0.76960193
## 6 0.5 0.76485788
## 7 0.6 0.75482094
## 8 0.7 0.74964235
## 9 0.8 0.70481928
## 10 0.9 0.59666667
## 11 1.0 0.05225653
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 375
## 2 Y 91
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 100
## 2 319
## Prediction
## Reference N Y
## N 375 100
## Y 91 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.841808e-01 5.666714e-01 7.555883e-01 8.108669e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.821830e-53 5.626839e-01
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 15.801 4.558
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.8008199
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9962198 1 0.59567 0.8385905
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7696019 0.7841808
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7555883 0.8108669 0.5666714
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01084091 0.02458222# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#model_id <- "Rank9.2"; indep_vars_vctr <- c(NULL
# ,"<feat1>"
# ,"<feat1>*<feat2>"
# ,"<feat1>:<feat2>"
# )
# for (method in c("bayesglm")) {
# ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(model_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
# }
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.log
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.log
## Max.cor.Y.rpart biddable, startprice.log
## Max.cor.Y.glm biddable, startprice.log
## Interact.High.cor.Y.glm biddable, startprice.log, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.351
## Random.myrandom_classfr 0 0.265
## Max.cor.Y.cv.0.rpart 0 0.612
## Max.cor.Y.cv.0.cp.0.rpart 0 0.477
## Max.cor.Y.rpart 3 1.004
## Max.cor.Y.glm 1 1.043
## Interact.High.cor.Y.glm 1 1.015
## Low.cor.X.glm 1 1.427
## All.X.glm 1 1.453
## All.X.bayesglm 1 2.635
## All.X.glmnet 9 5.319
## All.X.no.rnorm.rpart 3 1.562
## All.X.no.rnorm.rf 3 15.801
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.002 0.5000000
## Random.myrandom_classfr 0.002 0.5071756
## Max.cor.Y.cv.0.rpart 0.012 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.008 0.8781425
## Max.cor.Y.rpart 0.012 0.8172434
## Max.cor.Y.glm 0.015 0.8402290
## Interact.High.cor.Y.glm 0.014 0.8427523
## Low.cor.X.glm 0.159 0.8851187
## All.X.glm 0.221 0.8949364
## All.X.bayesglm 0.362 0.8908779
## All.X.glmnet 0.579 0.8742070
## All.X.no.rnorm.rpart 0.062 0.8214419
## All.X.no.rnorm.rf 4.558 1.0000000
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320225
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.8048246
## Max.cor.Y.rpart 0.5 0.7628866
## Max.cor.Y.glm 0.5 0.7570621
## Interact.High.cor.Y.glm 0.4 0.7563396
## Low.cor.X.glm 0.5 0.7944573
## All.X.glm 0.5 0.8004587
## All.X.bayesglm 0.5 0.8004587
## All.X.glmnet 0.5 0.7773933
## All.X.no.rnorm.rpart 0.6 0.7821901
## All.X.no.rnorm.rf 0.5 1.0000000
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.5379877 0.5060896
## Random.myrandom_classfr 0.4620123 0.4303445
## Max.cor.Y.cv.0.rpart 0.5379877 0.5060896
## Max.cor.Y.cv.0.cp.0.rpart 0.8172485 0.7915181
## Max.cor.Y.rpart 0.7731117 0.7604190
## Max.cor.Y.glm 0.7803039 0.7518769
## Interact.High.cor.Y.glm 0.7690028 0.7454801
## Low.cor.X.glm 0.7618107 0.7915181
## All.X.glm 0.7515416 0.7958255
## All.X.bayesglm 0.7669516 0.7958255
## All.X.glmnet 0.7782494 0.7754050
## All.X.no.rnorm.rpart 0.8069991 0.7882906
## All.X.no.rnorm.rf 0.8008199 0.9962198
## max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5696555 0.0000000 0.5000000
## Random.myrandom_classfr 0.4939104 0.0000000 0.5191913
## Max.cor.Y.cv.0.rpart 0.5696555 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8410439 0.6330658 0.8075096
## Max.cor.Y.rpart 0.8127790 0.5377260 0.8080205
## Max.cor.Y.glm 0.8049446 0.5567446 0.8402773
## Interact.High.cor.Y.glm 0.7990590 0.5332090 0.8394608
## Low.cor.X.glm 0.8410439 0.5194818 0.8296945
## All.X.glm 0.8449246 0.4983565 0.8269987
## All.X.bayesglm 0.8449246 0.5295107 0.8355584
## All.X.glmnet 0.8264517 0.5520479 0.8374685
## All.X.no.rnorm.rpart 0.8381305 0.6066762 0.8103723
## All.X.no.rnorm.rf 1.0000000 0.5956700 0.8385905
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6332046
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.6 0.7439490
## Max.cor.Y.rpart 0.5 0.7582697
## Max.cor.Y.glm 0.4 0.7604938
## Interact.High.cor.Y.glm 0.4 0.7636816
## Low.cor.X.glm 0.5 0.7580026
## All.X.glm 0.6 0.7576975
## All.X.bayesglm 0.6 0.7630522
## All.X.glmnet 0.5 0.7564103
## All.X.no.rnorm.rpart 0.6 0.7600000
## All.X.no.rnorm.rf 0.4 0.7696019
## max.Accuracy.OOB max.AccuracyLower.OOB
## MFO.myMFO_classfr 0.5367232 0.5032294
## Random.myrandom_classfr 0.4632768 0.4300283
## Max.cor.Y.cv.0.rpart 0.5367232 0.5032294
## Max.cor.Y.cv.0.cp.0.rpart 0.7728814 0.7438293
## Max.cor.Y.rpart 0.7853107 0.7567658
## Max.cor.Y.glm 0.7807910 0.7520575
## Interact.High.cor.Y.glm 0.7853107 0.7567658
## Low.cor.X.glm 0.7864407 0.7579436
## All.X.glm 0.7954802 0.7673772
## All.X.bayesglm 0.8000000 0.7721016
## All.X.glmnet 0.7853107 0.7567658
## All.X.no.rnorm.rpart 0.7966102 0.7685578
## All.X.no.rnorm.rf 0.7841808 0.7555883
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5699717 0.0000000
## Random.myrandom_classfr 0.4967706 0.0000000
## Max.cor.Y.cv.0.rpart 0.5699717 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8001036 0.5406159
## Max.cor.Y.rpart 0.8119416 0.5658292
## Max.cor.Y.glm 0.8076410 0.5584673
## Interact.High.cor.Y.glm 0.8119416 0.5671369
## Low.cor.X.glm 0.8130160 0.5677516
## All.X.glm 0.8215999 0.5836701
## All.X.bayesglm 0.8258843 0.5928708
## All.X.glmnet 0.8119416 0.5653916
## All.X.no.rnorm.rpart 0.8226715 0.5861800
## All.X.no.rnorm.rf 0.8108669 0.5666714
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA NA
## Random.myrandom_classfr NA NA NA
## Max.cor.Y.cv.0.rpart NA NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA NA
## Max.cor.Y.rpart 0.009511979 0.02504360 NA
## Max.cor.Y.glm 0.014187500 0.02978891 943.1953
## Interact.High.cor.Y.glm 0.013180511 0.02966064 946.8663
## Low.cor.X.glm 0.009749000 0.02418586 935.6464
## All.X.glm 0.009327212 0.01557657 944.2624
## All.X.bayesglm 0.012127519 0.02326004 974.6030
## All.X.glmnet 0.013047135 0.02731799 NA
## All.X.no.rnorm.rpart 0.016668501 0.03448591 NA
## All.X.no.rnorm.rf 0.010840914 0.02458222 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 fit.models_1_rf 6 0 140.939 160.449 19.51
## 7 fit.models_1_end 7 0 160.450 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 111.325 160.457 49.133
## 12 fit.models 7 2 160.458 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.log
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.log
## Max.cor.Y.rpart biddable, startprice.log
## Max.cor.Y.glm biddable, startprice.log
## Interact.High.cor.Y.glm biddable, startprice.log, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.ratio.nstopwrds.nwrds, biddable:D.npnct06.log, biddable:D.nchrs.log, biddable:D.nwrds.unq.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.5071756
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.8781425
## Max.cor.Y.rpart 3 0.8172434
## Max.cor.Y.glm 1 0.8402290
## Interact.High.cor.Y.glm 1 0.8427523
## Low.cor.X.glm 1 0.8851187
## All.X.glm 1 0.8949364
## All.X.bayesglm 1 0.8908779
## All.X.glmnet 9 0.8742070
## All.X.no.rnorm.rpart 3 0.8214419
## All.X.no.rnorm.rf 3 1.0000000
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320225
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.8048246
## Max.cor.Y.rpart 0.5 0.7628866
## Max.cor.Y.glm 0.5 0.7570621
## Interact.High.cor.Y.glm 0.4 0.7563396
## Low.cor.X.glm 0.5 0.7944573
## All.X.glm 0.5 0.8004587
## All.X.bayesglm 0.5 0.8004587
## All.X.glmnet 0.5 0.7773933
## All.X.no.rnorm.rpart 0.6 0.7821901
## All.X.no.rnorm.rf 0.5 1.0000000
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5379877 0.0000000 0.5000000
## Random.myrandom_classfr 0.4620123 0.0000000 0.5191913
## Max.cor.Y.cv.0.rpart 0.5379877 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8172485 0.6330658 0.8075096
## Max.cor.Y.rpart 0.7731117 0.5377260 0.8080205
## Max.cor.Y.glm 0.7803039 0.5567446 0.8402773
## Interact.High.cor.Y.glm 0.7690028 0.5332090 0.8394608
## Low.cor.X.glm 0.7618107 0.5194818 0.8296945
## All.X.glm 0.7515416 0.4983565 0.8269987
## All.X.bayesglm 0.7669516 0.5295107 0.8355584
## All.X.glmnet 0.7782494 0.5520479 0.8374685
## All.X.no.rnorm.rpart 0.8069991 0.6066762 0.8103723
## All.X.no.rnorm.rf 0.8008199 0.5956700 0.8385905
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6332046
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.6 0.7439490
## Max.cor.Y.rpart 0.5 0.7582697
## Max.cor.Y.glm 0.4 0.7604938
## Interact.High.cor.Y.glm 0.4 0.7636816
## Low.cor.X.glm 0.5 0.7580026
## All.X.glm 0.6 0.7576975
## All.X.bayesglm 0.6 0.7630522
## All.X.glmnet 0.5 0.7564103
## All.X.no.rnorm.rpart 0.6 0.7600000
## All.X.no.rnorm.rf 0.4 0.7696019
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5367232 0.0000000
## Random.myrandom_classfr 0.4632768 0.0000000
## Max.cor.Y.cv.0.rpart 0.5367232 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7728814 0.5406159
## Max.cor.Y.rpart 0.7853107 0.5658292
## Max.cor.Y.glm 0.7807910 0.5584673
## Interact.High.cor.Y.glm 0.7853107 0.5671369
## Low.cor.X.glm 0.7864407 0.5677516
## All.X.glm 0.7954802 0.5836701
## All.X.bayesglm 0.8000000 0.5928708
## All.X.glmnet 0.7853107 0.5653916
## All.X.no.rnorm.rpart 0.7966102 0.5861800
## All.X.no.rnorm.rf 0.7841808 0.5666714
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.myMFO_classfr 2.84900285 500.0000000
## Random.myrandom_classfr 3.77358491 500.0000000
## Max.cor.Y.cv.0.rpart 1.63398693 83.3333333
## Max.cor.Y.cv.0.cp.0.rpart 2.09643606 125.0000000
## Max.cor.Y.rpart 0.99601594 83.3333333
## Max.cor.Y.glm 0.95877277 66.6666667
## Interact.High.cor.Y.glm 0.98522167 71.4285714
## Low.cor.X.glm 0.70077085 6.2893082
## All.X.glm 0.68823125 4.5248869
## All.X.bayesglm 0.37950664 2.7624309
## All.X.glmnet 0.18800526 1.7271157
## All.X.no.rnorm.rpart 0.64020487 16.1290323
## All.X.no.rnorm.rf 0.06328713 0.2193945
## inv.aic.fit
## MFO.myMFO_classfr NA
## Random.myrandom_classfr NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.glm 0.001060226
## Interact.High.cor.Y.glm 0.001056115
## Low.cor.X.glm 0.001068780
## All.X.glm 0.001059028
## All.X.bayesglm 0.001026059
## All.X.glmnet NA
## All.X.no.rnorm.rpart NA
## All.X.no.rnorm.rf NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually if you must have them.## Warning: Removed 5 rows containing missing values (geom_path).## Warning: Removed 103 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 10 All.X.bayesglm 0.8000000 0.8355584 0.5928708
## 12 All.X.no.rnorm.rpart 0.7966102 0.8103723 0.5861800
## 9 All.X.glm 0.7954802 0.8269987 0.5836701
## 8 Low.cor.X.glm 0.7864407 0.8296945 0.5677516
## 7 Interact.High.cor.Y.glm 0.7853107 0.8394608 0.5671369
## 11 All.X.glmnet 0.7853107 0.8374685 0.5653916
## 5 Max.cor.Y.rpart 0.7853107 0.8080205 0.5658292
## 13 All.X.no.rnorm.rf 0.7841808 0.8385905 0.5666714
## 6 Max.cor.Y.glm 0.7807910 0.8402773 0.5584673
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7728814 0.8075096 0.5406159
## 1 MFO.myMFO_classfr 0.5367232 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5367232 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.4632768 0.5191913 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 10 974.6030 0.6
## 12 NA 0.6
## 9 944.2624 0.6
## 8 935.6464 0.5
## 7 946.8663 0.4
## 11 NA 0.5
## 5 NA 0.5
## 13 NA 0.4
## 6 943.1953 0.4
## 4 NA 0.6
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually if you must have them.## Warning: Removed 45 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.X.bayesglm"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4001 -0.6734 -0.2284 0.6303 2.3665
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 10.2842486 6.4893782
## biddable 1.7573338 0.1973230
## D.ratio.nstopwrds.nwrds -1.6600153 2.3190873
## D.npnct15.log 1.7243105 0.9036160
## D.npnct03.log 0.1709185 1.6482521
## D.terms.n.stem.stop.Ratio -5.3365408 5.2987561
## D.ratio.sum.TfIdf.nwrds -0.2975649 0.2792717
## .rnorm 0.0085918 0.0914511
## D.npnct01.log 0.0976625 0.5307216
## D.TfIdf.sum.stem.stop.Ratio 0.0820975 3.4101394
## storage.fctr16 -0.0312320 0.4034695
## storage.fctr32 -0.1607385 0.4366170
## storage.fctr64 0.5414545 0.4252873
## storage.fctrUnknown -0.1556108 0.5766591
## D.npnct11.log -0.0703486 0.3534029
## D.npnct10.log -7.4868921 7.5862091
## D.TfIdf.sum.post.stop 0.0536202 0.2889917
## D.TfIdf.sum.post.stem 0.0772985 0.3046543
## D.sum.TfIdf 0.0772985 0.3046543
## D.npnct13.log 0.0038540 0.3520800
## D.npnct08.log 0.6436918 0.6721688
## `prdline.my.fctriPad 1` 0.6129401 0.4905833
## `prdline.my.fctriPad 2` 0.5590282 0.4989079
## `prdline.my.fctriPad 3+` 0.9228015 0.4730864
## prdline.my.fctriPadAir 1.6331927 0.4867491
## prdline.my.fctriPadmini 0.8234679 0.4623652
## `prdline.my.fctriPadmini 2+` 1.1789282 0.5051392
## color.fctrBlack 0.1754209 0.2484243
## color.fctrGold -0.4232961 0.4938666
## `color.fctrSpace Gray` -0.1515756 0.3206617
## color.fctrWhite -0.0476805 0.2450863
## D.npnct16.log 2.0953668 1.6045127
## D.npnct24.log 0.0096372 2.3183564
## D.nstopwrds.log 0.7430813 0.6531890
## D.npnct06.log -3.9327506 1.9017435
## D.npnct28.log -0.0586218 2.1808018
## D.nuppr.log -0.0921862 0.4809059
## D.nchrs.log -0.1789244 0.4884053
## D.npnct12.log 0.5005917 0.7367023
## D.nwrds.log -0.0174159 0.7577466
## D.npnct09.log -1.8762155 4.8176276
## D.ndgts.log 0.6118216 0.4309216
## D.nwrds.unq.log -0.3662339 0.9899972
## `carrier.fctrAT&T` -0.0925699 0.7617184
## carrier.fctrOther 1.4827438 1.6840761
## carrier.fctrSprint 0.5934538 0.9178047
## `carrier.fctrT-Mobile` -1.0346543 1.0426395
## carrier.fctrUnknown -0.4669640 0.7673418
## carrier.fctrVerizon 0.2125612 0.7888289
## cellular.fctr1 0.1663025 0.7310228
## cellular.fctrUnknown 0.1023197 0.8486886
## D.npnct14.log -1.9746863 1.0751784
## D.terms.n.post.stem -0.1008951 0.1999550
## D.terms.n.post.stop -0.1183221 0.1987110
## D.npnct05.log -2.9551434 1.4986226
## `condition.fctrFor parts or not working` -0.3343721 0.3843888
## `condition.fctrManufacturer refurbished` 0.3922178 0.5731989
## condition.fctrNew -0.2738450 0.2966734
## `condition.fctrNew other (see details)` 0.0766691 0.4358370
## `condition.fctrSeller refurbished` -0.8332128 0.4550953
## idseq.my -0.0004650 0.0002007
## startprice.log -0.9436826 0.1330383
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.9712145 0.6807140
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.8582323 0.7384926
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.6062147 0.6726430
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.0755689 0.7438557
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.3754393 0.5611276
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.8918425 0.7006279
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.0683592 0.8317439
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.3231263 0.9057692
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.2816765 0.7876395
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.8031878 1.5583563
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.3338476 0.6084017
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.6236151 0.8101234
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.7536847 0.7745361
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.4381060 0.7847287
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.6472311 0.8451679
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.7940189 1.1468944
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.8093186 0.7297984
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.5468567 0.8348141
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.7465581 1.1464691
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.7231388 1.1060445
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.0000000 2.5000000
## z value Pr(>|z|)
## (Intercept) 1.585 0.113016
## biddable 8.906 < 2e-16 ***
## D.ratio.nstopwrds.nwrds -0.716 0.474112
## D.npnct15.log 1.908 0.056361 .
## D.npnct03.log 0.104 0.917410
## D.terms.n.stem.stop.Ratio -1.007 0.313872
## D.ratio.sum.TfIdf.nwrds -1.066 0.286648
## .rnorm 0.094 0.925149
## D.npnct01.log 0.184 0.853999
## D.TfIdf.sum.stem.stop.Ratio 0.024 0.980793
## storage.fctr16 -0.077 0.938298
## storage.fctr32 -0.368 0.712765
## storage.fctr64 1.273 0.202965
## storage.fctrUnknown -0.270 0.787276
## D.npnct11.log -0.199 0.842215
## D.npnct10.log -0.987 0.323688
## D.TfIdf.sum.post.stop 0.186 0.852804
## D.TfIdf.sum.post.stem 0.254 0.799708
## D.sum.TfIdf 0.254 0.799708
## D.npnct13.log 0.011 0.991266
## D.npnct08.log 0.958 0.338247
## `prdline.my.fctriPad 1` 1.249 0.211515
## `prdline.my.fctriPad 2` 1.121 0.262499
## `prdline.my.fctriPad 3+` 1.951 0.051105 .
## prdline.my.fctriPadAir 3.355 0.000793 ***
## prdline.my.fctriPadmini 1.781 0.074914 .
## `prdline.my.fctriPadmini 2+` 2.334 0.019603 *
## color.fctrBlack 0.706 0.480105
## color.fctrGold -0.857 0.391386
## `color.fctrSpace Gray` -0.473 0.636430
## color.fctrWhite -0.195 0.845749
## D.npnct16.log 1.306 0.191579
## D.npnct24.log 0.004 0.996683
## D.nstopwrds.log 1.138 0.255279
## D.npnct06.log -2.068 0.038643 *
## D.npnct28.log -0.027 0.978555
## D.nuppr.log -0.192 0.847983
## D.nchrs.log -0.366 0.714108
## D.npnct12.log 0.680 0.496819
## D.nwrds.log -0.023 0.981663
## D.npnct09.log -0.389 0.696945
## D.ndgts.log 1.420 0.155666
## D.nwrds.unq.log -0.370 0.711431
## `carrier.fctrAT&T` -0.122 0.903273
## carrier.fctrOther 0.880 0.378616
## carrier.fctrSprint 0.647 0.517890
## `carrier.fctrT-Mobile` -0.992 0.321031
## carrier.fctrUnknown -0.609 0.542824
## carrier.fctrVerizon 0.269 0.787572
## cellular.fctr1 0.227 0.820041
## cellular.fctrUnknown 0.121 0.904038
## D.npnct14.log -1.837 0.066267 .
## D.terms.n.post.stem -0.505 0.613847
## D.terms.n.post.stop -0.595 0.551544
## D.npnct05.log -1.972 0.048620 *
## `condition.fctrFor parts or not working` -0.870 0.384366
## `condition.fctrManufacturer refurbished` 0.684 0.493810
## condition.fctrNew -0.923 0.355980
## `condition.fctrNew other (see details)` 0.176 0.860363
## `condition.fctrSeller refurbished` -1.831 0.067122 .
## idseq.my -2.317 0.020502 *
## startprice.log -7.093 1.31e-12 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.896 0.003782 **
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.162 0.245178
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.901 0.367459
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.446 0.148195
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.669 0.503444
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.273 0.203047
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.082 0.934497
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.357 0.721285
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.358 0.720627
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.157 0.247228
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.549 0.583192
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.770 0.441432
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.973 0.330514
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.558 0.576647
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.949 0.051296 .
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.692 0.488736
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.109 0.267447
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.655 0.512427
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.396 0.016590 *
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.654 0.513237
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.6 on 973 degrees of freedom
## Residual deviance: 794.6 on 884 degrees of freedom
## AIC: 974.6
##
## Number of Fisher Scoring iterations: 16## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## N importance
## startprice.log 100.000000 100.000000
## biddable 94.720022 94.720022
## idseq.my 48.121762 48.121762
## prdline.my.fctr 32.302503 32.302503
## D.ratio.nstopwrds.nwrds 26.323096 26.323096
## D.TfIdf.sum.stem.stop.Ratio 23.352887 23.352887
## .rnorm 20.456519 20.456519
## D.npnct15.log 19.859878 19.859878
## D.npnct03.log 19.699198 19.699198
## D.terms.n.stem.stop.Ratio 19.469403 19.469403
## D.npnct01.log 18.926519 18.926519
## storage.fctr 18.264306 18.264306
## D.npnct28.log 18.184557 18.184557
## D.ratio.sum.TfIdf.nwrds 17.889190 17.889190
## D.npnct10.log 17.652897 17.652897
## D.npnct09.log 17.387066 17.387066
## D.npnct11.log 16.045510 16.045510
## D.npnct08.log 16.026016 16.026016
## D.TfIdf.sum.post.stop 15.682209 15.682209
## D.npnct12.log 15.644402 15.644402
## D.TfIdf.sum.post.stem 15.228525 15.228525
## D.sum.TfIdf 15.228525 15.228525
## D.npnct16.log 15.122193 15.122193
## D.npnct06.log 14.718131 14.718131
## D.ndgts.log 13.597509 13.597509
## D.npnct05.log 12.955382 12.955382
## D.npnct24.log 12.845505 12.845505
## D.npnct14.log 12.808880 12.808880
## D.npnct13.log 12.568451 12.568451
## D.nstopwrds.log 10.795659 10.795659
## .clusterid.fctr 10.171254 10.171254
## D.nwrds.log 9.988717 9.988717
## color.fctr 9.241439 9.241439
## D.nuppr.log 8.117272 8.117272
## D.nchrs.log 7.930601 7.930601
## carrier.fctr 6.640438 6.640438
## D.terms.n.post.stop 6.303720 6.303720
## D.nwrds.unq.log 6.158399 6.158399
## D.terms.n.post.stem 6.158399 6.158399
## cellular.fctr 4.979886 4.979886
## condition.fctr 0.000000 0.000000
## All.X.bayesglm.importance
## startprice.log 100.000000
## biddable 94.720022
## idseq.my 48.121762
## prdline.my.fctr 32.302503
## D.ratio.nstopwrds.nwrds 26.323096
## D.TfIdf.sum.stem.stop.Ratio 23.352887
## .rnorm 20.456519
## D.npnct15.log 19.859878
## D.npnct03.log 19.699198
## D.terms.n.stem.stop.Ratio 19.469403
## D.npnct01.log 18.926519
## storage.fctr 18.264306
## D.npnct28.log 18.184557
## D.ratio.sum.TfIdf.nwrds 17.889190
## D.npnct10.log 17.652897
## D.npnct09.log 17.387066
## D.npnct11.log 16.045510
## D.npnct08.log 16.026016
## D.TfIdf.sum.post.stop 15.682209
## D.npnct12.log 15.644402
## D.TfIdf.sum.post.stem 15.228525
## D.sum.TfIdf 15.228525
## D.npnct16.log 15.122193
## D.npnct06.log 14.718131
## D.ndgts.log 13.597509
## D.npnct05.log 12.955382
## D.npnct24.log 12.845505
## D.npnct14.log 12.808880
## D.npnct13.log 12.568451
## D.nstopwrds.log 10.795659
## .clusterid.fctr 10.171254
## D.nwrds.log 9.988717
## color.fctr 9.241439
## D.nuppr.log 8.117272
## D.nchrs.log 7.930601
## carrier.fctr 6.640438
## D.terms.n.post.stop 6.303720
## D.nwrds.unq.log 6.158399
## D.terms.n.post.stem 6.158399
## cellular.fctr 4.979886
## condition.fctr 0.000000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 41
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.All.X.bayesglm.prob
## 66 10066 N 0.03500128
## 1396 11397 N 0.04164352
## 285 10285 Y 0.99992051
## 1416 11417 N 0.62937896
## 1699 11700 N 0.65233489
## 537 10537 N 0.68403197
## 1768 11769 N 0.70369093
## 409 10409 N 0.70460421
## 1356 11357 N 0.71335357
## 199 10199 N 0.76989832
## 282 10282 N 0.78310867
## 127 10127 N 0.78349682
## 254 10254 N 0.78460014
## 851 10851 N 0.81725329
## 283 10283 N 0.98760455
## sold.fctr.predict.All.X.bayesglm
## 66 N
## 1396 N
## 285 Y
## 1416 Y
## 1699 Y
## 537 Y
## 1768 Y
## 409 Y
## 1356 Y
## 199 Y
## 282 Y
## 127 Y
## 254 Y
## 851 Y
## 283 Y
## sold.fctr.predict.All.X.bayesglm.accurate
## 66 TRUE
## 1396 TRUE
## 285 TRUE
## 1416 FALSE
## 1699 FALSE
## 537 FALSE
## 1768 FALSE
## 409 FALSE
## 1356 FALSE
## 199 FALSE
## 282 FALSE
## 127 FALSE
## 254 FALSE
## 851 FALSE
## 283 FALSE
## sold.fctr.predict.All.X.bayesglm.error .label
## 66 0.00000000 10066
## 1396 0.00000000 11397
## 285 0.00000000 10285
## 1416 0.02937896 11417
## 1699 0.05233489 11700
## 537 0.08403197 10537
## 1768 0.10369093 11769
## 409 0.10460421 10409
## 1356 0.11335357 11357
## 199 0.16989832 10199
## 282 0.18310867 10282
## 127 0.18349682 10127
## 254 0.18460014 10254
## 851 0.21725329 10851
## 283 0.38760455 10283
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.All.X.bayesglm.prob
## 15 10015 Y 0.000104908
## 1855 11857 Y 0.001135559
## 1803 11804 Y 0.012483692
## 1704 11705 Y 0.015222047
## 1358 11359 Y 0.016490237
## 178 10178 Y 0.016948223
## sold.fctr.predict.All.X.bayesglm
## 15 N
## 1855 N
## 1803 N
## 1704 N
## 1358 N
## 178 N
## sold.fctr.predict.All.X.bayesglm.accurate
## 15 FALSE
## 1855 FALSE
## 1803 FALSE
## 1704 FALSE
## 1358 FALSE
## 178 FALSE
## sold.fctr.predict.All.X.bayesglm.error
## 15 -0.5998951
## 1855 -0.5988644
## 1803 -0.5875163
## 1704 -0.5847780
## 1358 -0.5835098
## 178 -0.5830518
## UniqueID sold.fctr sold.fctr.predict.All.X.bayesglm.prob
## 1724 11725 Y 0.09574963
## 675 10675 Y 0.12537046
## 27 10027 Y 0.33584920
## 141 10141 Y 0.37080708
## 1481 11482 N 0.77169427
## 748 10748 N 0.85395829
## sold.fctr.predict.All.X.bayesglm
## 1724 N
## 675 N
## 27 N
## 141 N
## 1481 Y
## 748 Y
## sold.fctr.predict.All.X.bayesglm.accurate
## 1724 FALSE
## 675 FALSE
## 27 FALSE
## 141 FALSE
## 1481 FALSE
## 748 FALSE
## sold.fctr.predict.All.X.bayesglm.error
## 1724 -0.5042504
## 675 -0.4746295
## 27 -0.2641508
## 141 -0.2291929
## 1481 0.1716943
## 748 0.2539583
## UniqueID sold.fctr sold.fctr.predict.All.X.bayesglm.prob
## 347 10347 N 0.8539850
## 488 10488 N 0.9007541
## 526 10526 N 0.9038049
## 1431 11432 N 0.9394273
## 283 10283 N 0.9876045
## 491 10491 N 0.9903817
## sold.fctr.predict.All.X.bayesglm
## 347 Y
## 488 Y
## 526 Y
## 1431 Y
## 283 Y
## 491 Y
## sold.fctr.predict.All.X.bayesglm.accurate
## 347 FALSE
## 488 FALSE
## 526 FALSE
## 1431 FALSE
## 283 FALSE
## 491 FALSE
## sold.fctr.predict.All.X.bayesglm.error
## 347 0.2539850
## 488 0.3007541
## 526 0.3038049
## 1431 0.3394273
## 283 0.3876045
## 491 0.3903817
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 160.458 177.159 16.701
## 13 fit.models 7 3 177.160 NA NA if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.models_3: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.All.X.bayesglm.prob"
## [2] "sold.fctr.predict.All.X.bayesglm"
## [3] "sold.fctr.predict.All.X.bayesglm.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 177.160 181.481 4.321
## 14 fit.data.training 8 0 181.481 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.data.training_0: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## [1] "fitting model: Final.bayesglm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4691 -0.6900 -0.3200 0.6479 2.8499
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 8.3033892 4.9300667
## biddable 1.5370896 0.1389684
## D.ratio.nstopwrds.nwrds -1.9658593 2.1412018
## D.npnct15.log 1.9776754 0.7385301
## D.npnct03.log -0.6933728 1.0359646
## D.terms.n.stem.stop.Ratio 0.2525043 3.9365184
## D.ratio.sum.TfIdf.nwrds 0.0742532 0.2125512
## .rnorm 0.0147608 0.0640771
## D.npnct01.log 0.2214312 0.4051376
## D.TfIdf.sum.stem.stop.Ratio -2.3800888 2.8078889
## storage.fctr16 0.1942966 0.3208871
## storage.fctr32 0.1133594 0.3415676
## storage.fctr64 0.4264412 0.3311840
## storage.fctrUnknown 0.5174260 0.4504924
## D.npnct11.log 0.0305816 0.2510956
## D.npnct10.log 0.5434612 1.3040951
## D.TfIdf.sum.post.stop -0.0393093 0.2859242
## D.TfIdf.sum.post.stem 0.0295285 0.3020463
## D.sum.TfIdf 0.0295285 0.3020463
## D.npnct13.log -0.1296193 0.2449487
## D.npnct08.log 0.0387829 0.4873544
## `prdline.my.fctriPad 1` 0.0520009 0.3631591
## `prdline.my.fctriPad 2` 0.3645185 0.3562388
## `prdline.my.fctriPad 3+` 0.7440688 0.3553927
## prdline.my.fctriPadAir 1.4886746 0.3597259
## prdline.my.fctriPadmini 0.3868539 0.3450205
## `prdline.my.fctriPadmini 2+` 1.1196646 0.3664707
## color.fctrBlack 0.1677916 0.1766165
## color.fctrGold -0.2965323 0.3707394
## `color.fctrSpace Gray` 0.0398129 0.2228328
## color.fctrWhite 0.0220017 0.1702262
## D.npnct16.log 1.1738357 1.1456876
## D.npnct24.log -1.8905008 2.1472294
## D.nstopwrds.log 0.7888635 0.6077276
## D.npnct06.log -1.6225092 1.2723778
## D.npnct28.log -1.5357164 9.2880968
## D.nuppr.log -0.0238192 0.4364738
## D.nchrs.log -0.1320022 0.4620742
## D.npnct12.log -0.0984848 0.4860841
## D.nwrds.log 0.2501422 0.7119122
## D.npnct09.log -1.8386426 6.0058599
## D.ndgts.log 0.2764099 0.2793421
## D.nwrds.unq.log -0.0300525 0.8782860
## `carrier.fctrAT&T` -0.1718169 0.7191824
## carrier.fctrOther 1.5990700 1.5992110
## carrier.fctrSprint 0.3033087 0.8078622
## `carrier.fctrT-Mobile` -0.3892784 0.8810030
## carrier.fctrUnknown -0.4521347 0.7213488
## carrier.fctrVerizon -0.0110885 0.7275100
## cellular.fctr1 0.2814709 0.7026379
## cellular.fctrUnknown -0.0215896 0.7681777
## D.npnct14.log -0.7171160 0.5832986
## D.terms.n.post.stem -0.0584319 0.1918298
## D.terms.n.post.stop -0.1255129 0.1914798
## D.npnct05.log -2.1850166 1.1036368
## `condition.fctrFor parts or not working` -0.1001831 0.2723663
## `condition.fctrManufacturer refurbished` 0.1656301 0.4323168
## condition.fctrNew -0.1949726 0.2135520
## `condition.fctrNew other (see details)` 0.0957792 0.3049501
## `condition.fctrSeller refurbished` -0.6527678 0.3112225
## idseq.my -0.0005699 0.0001413
## startprice.log -1.0681319 0.1092686
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.3818867 0.4898861
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.1008549 0.5582433
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.2993927 0.4333178
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.0003153 0.5092025
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.3846058 0.4120187
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.0836998 0.5685292
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.0674316 0.6482009
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.3206917 0.7596466
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.0326264 0.6061938
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.3792366 0.6731810
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.5456619 0.4906993
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.3396675 0.6205177
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.7482879 0.5679204
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.0625266 0.5715438
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -0.7812584 0.6760323
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.9145859 0.7019221
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.7380543 0.5423606
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.0178703 0.6206451
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.3837747 0.6847777
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.4971432 0.7960420
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.0000000 2.5000000
## z value Pr(>|z|)
## (Intercept) 1.684 0.09214 .
## biddable 11.061 < 2e-16 ***
## D.ratio.nstopwrds.nwrds -0.918 0.35856
## D.npnct15.log 2.678 0.00741 **
## D.npnct03.log -0.669 0.50330
## D.terms.n.stem.stop.Ratio 0.064 0.94886
## D.ratio.sum.TfIdf.nwrds 0.349 0.72683
## .rnorm 0.230 0.81781
## D.npnct01.log 0.547 0.58468
## D.TfIdf.sum.stem.stop.Ratio -0.848 0.39664
## storage.fctr16 0.605 0.54485
## storage.fctr32 0.332 0.73998
## storage.fctr64 1.288 0.19788
## storage.fctrUnknown 1.149 0.25073
## D.npnct11.log 0.122 0.90306
## D.npnct10.log 0.417 0.67687
## D.TfIdf.sum.post.stop -0.137 0.89065
## D.TfIdf.sum.post.stem 0.098 0.92212
## D.sum.TfIdf 0.098 0.92212
## D.npnct13.log -0.529 0.59669
## D.npnct08.log 0.080 0.93657
## `prdline.my.fctriPad 1` 0.143 0.88614
## `prdline.my.fctriPad 2` 1.023 0.30619
## `prdline.my.fctriPad 3+` 2.094 0.03629 *
## prdline.my.fctriPadAir 4.138 3.50e-05 ***
## prdline.my.fctriPadmini 1.121 0.26218
## `prdline.my.fctriPadmini 2+` 3.055 0.00225 **
## color.fctrBlack 0.950 0.34210
## color.fctrGold -0.800 0.42380
## `color.fctrSpace Gray` 0.179 0.85820
## color.fctrWhite 0.129 0.89716
## D.npnct16.log 1.025 0.30557
## D.npnct24.log -0.880 0.37862
## D.nstopwrds.log 1.298 0.19427
## D.npnct06.log -1.275 0.20225
## D.npnct28.log -0.165 0.86867
## D.nuppr.log -0.055 0.95648
## D.nchrs.log -0.286 0.77513
## D.npnct12.log -0.203 0.83944
## D.nwrds.log 0.351 0.72531
## D.npnct09.log -0.306 0.75950
## D.ndgts.log 0.990 0.32242
## D.nwrds.unq.log -0.034 0.97270
## `carrier.fctrAT&T` -0.239 0.81118
## carrier.fctrOther 1.000 0.31735
## carrier.fctrSprint 0.375 0.70733
## `carrier.fctrT-Mobile` -0.442 0.65859
## carrier.fctrUnknown -0.627 0.53080
## carrier.fctrVerizon -0.015 0.98784
## cellular.fctr1 0.401 0.68872
## cellular.fctrUnknown -0.028 0.97758
## D.npnct14.log -1.229 0.21892
## D.terms.n.post.stem -0.305 0.76067
## D.terms.n.post.stop -0.655 0.51215
## D.npnct05.log -1.980 0.04772 *
## `condition.fctrFor parts or not working` -0.368 0.71300
## `condition.fctrManufacturer refurbished` 0.383 0.70163
## condition.fctrNew -0.913 0.36124
## `condition.fctrNew other (see details)` 0.314 0.75346
## `condition.fctrSeller refurbished` -2.097 0.03596 *
## idseq.my -4.034 5.49e-05 ***
## startprice.log -9.775 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.821 0.00479 **
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.181 0.85663
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.691 0.48961
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -1.964 0.04948 *
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.933 0.35058
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.147 0.88296
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.104 0.91715
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.739 0.08211 .
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.054 0.95708
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.563 0.57320
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -1.112 0.26613
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.547 0.58411
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.318 0.18764
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.109 0.91289
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.00000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.156 0.24782
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.303 0.19258
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.361 0.17357
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.000 1.00000
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.029 0.97703
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.00000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.00000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.00000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.021 0.04330 *
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000 1.00000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.00000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.625 0.53229
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2566.7 on 1858 degrees of freedom
## Residual deviance: 1608.1 on 1769 degrees of freedom
## AIC: 1788.1
##
## Number of Fisher Scoring iterations: 13
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6325855
## 2 0.1 0.6773163
## 3 0.2 0.7309312
## 4 0.3 0.7625133
## 5 0.4 0.7762399
## 6 0.5 0.7814408
## 7 0.6 0.7840617
## 8 0.7 0.7429359
## 9 0.8 0.6326373
## 10 0.9 0.4424460
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Final.bayesglm.N
## 1 N 913
## 2 Y 250
## sold.fctr.predict.Final.bayesglm.Y
## 1 86
## 2 610
## Prediction
## Reference N Y
## N 913 86
## Y 250 610
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.192577e-01 6.315955e-01 8.009972e-01 8.365047e-01 5.373857e-01
## AccuracyPValue McnemarPValue
## 5.457450e-144 5.981426e-19## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.bayesglm bayesglm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.npnct13.log, D.npnct08.log, prdline.my.fctr, color.fctr, D.npnct16.log, D.npnct24.log, D.nstopwrds.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.npnct12.log, D.nwrds.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, carrier.fctr, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.log, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.277 0.5
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8739914 0.6 0.7840617 0.7821373
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit min.aic.fit
## 1 0.8009972 0.8365047 0.5594478 1788.075
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009931481 0.01992347rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 181.481 186.502 5.021
## 15 fit.data.training 8 1 186.502 NA NA#```
#```{r fit.data.training_1, cache=FALSE}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.6sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## N.x All.X.bayesglm.importance
## startprice.log 100.000000 100.000000
## biddable 94.720022 94.720022
## idseq.my 48.121762 48.121762
## D.ratio.nstopwrds.nwrds 26.323096 26.323096
## D.TfIdf.sum.stem.stop.Ratio 23.352887 23.352887
## D.npnct15.log 19.859878 19.859878
## D.terms.n.stem.stop.Ratio 19.469403 19.469403
## D.npnct03.log 19.699198 19.699198
## D.npnct01.log 18.926519 18.926519
## D.npnct10.log 17.652897 17.652897
## .rnorm 20.456519 20.456519
## storage.fctr 18.264306 18.264306
## D.npnct28.log 18.184557 18.184557
## D.npnct09.log 17.387066 17.387066
## D.npnct11.log 16.045510 16.045510
## D.npnct08.log 16.026016 16.026016
## D.npnct12.log 15.644402 15.644402
## D.ratio.sum.TfIdf.nwrds 17.889190 17.889190
## D.npnct16.log 15.122193 15.122193
## D.npnct06.log 14.718131 14.718131
## D.npnct14.log 12.808880 12.808880
## D.TfIdf.sum.post.stop 15.682209 15.682209
## D.ndgts.log 13.597509 13.597509
## D.TfIdf.sum.post.stem 15.228525 15.228525
## D.sum.TfIdf 15.228525 15.228525
## D.npnct05.log 12.955382 12.955382
## D.npnct13.log 12.568451 12.568451
## color.fctr 9.241439 9.241439
## D.npnct24.log 12.845505 12.845505
## D.nstopwrds.log 10.795659 10.795659
## .clusterid.fctr 10.171254 10.171254
## prdline.my.fctr 32.302503 32.302503
## D.nwrds.log 9.988717 9.988717
## D.nuppr.log 8.117272 8.117272
## D.nchrs.log 7.930601 7.930601
## carrier.fctr 6.640438 6.640438
## cellular.fctr 4.979886 4.979886
## D.terms.n.post.stop 6.303720 6.303720
## D.nwrds.unq.log 6.158399 6.158399
## D.terms.n.post.stem 6.158399 6.158399
## condition.fctr 0.000000 0.000000
## N.y importance
## startprice.log 100.000000 100.000000
## biddable 95.026117 95.026117
## idseq.my 49.511380 49.511380
## D.ratio.nstopwrds.nwrds 27.012354 27.012354
## D.TfIdf.sum.stem.stop.Ratio 22.182931 22.182931
## D.npnct15.log 20.409144 20.409144
## D.terms.n.stem.stop.Ratio 20.407526 20.407526
## D.npnct03.log 19.628451 19.628451
## D.npnct01.log 19.349887 19.349887
## D.npnct10.log 18.632122 18.632122
## .rnorm 18.288850 18.288850
## storage.fctr 18.248570 18.248570
## D.npnct28.log 18.170112 18.170112
## D.npnct09.log 17.752750 17.752750
## D.npnct11.log 17.304168 17.304168
## D.npnct08.log 16.596594 16.596594
## D.npnct12.log 16.183600 16.183600
## D.ratio.sum.TfIdf.nwrds 16.015847 16.015847
## D.npnct16.log 16.010185 16.010185
## D.npnct06.log 15.782577 15.782577
## D.npnct14.log 15.280287 15.280287
## D.TfIdf.sum.post.stop 14.864382 14.864382
## D.ndgts.log 14.605229 14.605229
## D.TfIdf.sum.post.stem 14.423240 14.423240
## D.sum.TfIdf 14.423240 14.423240
## D.npnct05.log 13.947319 13.947319
## D.npnct13.log 13.585120 13.585120
## color.fctr 13.332115 13.332115
## D.npnct24.log 12.688763 12.688763
## D.nstopwrds.log 12.403890 12.403890
## .clusterid.fctr 11.936703 11.936703
## prdline.my.fctr 11.912115 11.912115
## D.nwrds.log 10.911417 10.911417
## D.nuppr.log 9.940809 9.940809
## D.nchrs.log 9.277722 9.277722
## carrier.fctr 9.121777 9.121777
## cellular.fctr 8.675136 8.675136
## D.terms.n.post.stop 8.265215 8.265215
## D.nwrds.unq.log 8.221215 8.221215
## D.terms.n.post.stem 8.221215 8.221215
## condition.fctr 0.000000 0.000000
## Final.bayesglm.importance
## startprice.log 100.000000
## biddable 95.026117
## idseq.my 49.511380
## D.ratio.nstopwrds.nwrds 27.012354
## D.TfIdf.sum.stem.stop.Ratio 22.182931
## D.npnct15.log 20.409144
## D.terms.n.stem.stop.Ratio 20.407526
## D.npnct03.log 19.628451
## D.npnct01.log 19.349887
## D.npnct10.log 18.632122
## .rnorm 18.288850
## storage.fctr 18.248570
## D.npnct28.log 18.170112
## D.npnct09.log 17.752750
## D.npnct11.log 17.304168
## D.npnct08.log 16.596594
## D.npnct12.log 16.183600
## D.ratio.sum.TfIdf.nwrds 16.015847
## D.npnct16.log 16.010185
## D.npnct06.log 15.782577
## D.npnct14.log 15.280287
## D.TfIdf.sum.post.stop 14.864382
## D.ndgts.log 14.605229
## D.TfIdf.sum.post.stem 14.423240
## D.sum.TfIdf 14.423240
## D.npnct05.log 13.947319
## D.npnct13.log 13.585120
## color.fctr 13.332115
## D.npnct24.log 12.688763
## D.nstopwrds.log 12.403890
## .clusterid.fctr 11.936703
## prdline.my.fctr 11.912115
## D.nwrds.log 10.911417
## D.nuppr.log 9.940809
## D.nchrs.log 9.277722
## carrier.fctr 9.121777
## cellular.fctr 8.675136
## D.terms.n.post.stop 8.265215
## D.nwrds.unq.log 8.221215
## D.terms.n.post.stem 8.221215
## condition.fctr 0.000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 41
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 15 10015 Y 0.06652246
## 11 10011 Y 0.18308575
## 17 10017 Y 0.25749470
## 141 10141 Y 0.35574103
## 3 10003 Y 0.37851102
## 14 10014 Y 0.49592550
## 2 10002 Y 0.99793570
## 91 10091 Y 0.99997322
## 1396 11397 N 0.03031021
## 135 10135 N 0.60257264
## 1 10001 N 0.65335221
## 127 10127 N 0.72540687
## 120 10120 N 0.76598889
## 182 10182 N 0.81245804
## 103 10103 N 0.88154075
## sold.fctr.predict.Final.bayesglm
## 15 N
## 11 N
## 17 N
## 141 N
## 3 N
## 14 N
## 2 Y
## 91 Y
## 1396 N
## 135 Y
## 1 Y
## 127 Y
## 120 Y
## 182 Y
## 103 Y
## sold.fctr.predict.Final.bayesglm.accurate
## 15 FALSE
## 11 FALSE
## 17 FALSE
## 141 FALSE
## 3 FALSE
## 14 FALSE
## 2 TRUE
## 91 TRUE
## 1396 TRUE
## 135 FALSE
## 1 FALSE
## 127 FALSE
## 120 FALSE
## 182 FALSE
## 103 FALSE
## sold.fctr.predict.Final.bayesglm.error .label
## 15 -0.533477543 10015
## 11 -0.416914253 10011
## 17 -0.342505298 10017
## 141 -0.244258967 10141
## 3 -0.221488982 10003
## 14 -0.104074496 10014
## 2 0.000000000 10002
## 91 0.000000000 10091
## 1396 0.000000000 11397
## 135 0.002572636 10135
## 1 0.053352213 10001
## 127 0.125406871 10127
## 120 0.165988886 10120
## 182 0.212458037 10182
## 103 0.281540745 10103
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 1704 11705 Y 0.01723298
## 1358 11359 Y 0.02804533
## 1803 11804 Y 0.04235968
## 15 10015 Y 0.06652246
## 1523 11524 Y 0.07340917
## 1783 11784 Y 0.07751727
## sold.fctr.predict.Final.bayesglm
## 1704 N
## 1358 N
## 1803 N
## 15 N
## 1523 N
## 1783 N
## sold.fctr.predict.Final.bayesglm.accurate
## 1704 FALSE
## 1358 FALSE
## 1803 FALSE
## 15 FALSE
## 1523 FALSE
## 1783 FALSE
## sold.fctr.predict.Final.bayesglm.error
## 1704 -0.5827670
## 1358 -0.5719547
## 1803 -0.5576403
## 15 -0.5334775
## 1523 -0.5265908
## 1783 -0.5224827
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 1730 11731 Y 0.1618909
## 312 10312 Y 0.2454240
## 717 10717 Y 0.2678947
## 37 10037 Y 0.3645673
## 562 10562 Y 0.4660657
## 902 10902 Y 0.4708093
## sold.fctr.predict.Final.bayesglm
## 1730 N
## 312 N
## 717 N
## 37 N
## 562 N
## 902 N
## sold.fctr.predict.Final.bayesglm.accurate
## 1730 FALSE
## 312 FALSE
## 717 FALSE
## 37 FALSE
## 562 FALSE
## 902 FALSE
## sold.fctr.predict.Final.bayesglm.error
## 1730 -0.4381091
## 312 -0.3545760
## 717 -0.3321053
## 37 -0.2354327
## 562 -0.1339343
## 902 -0.1291907
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 1390 11391 N 0.9264140
## 283 10283 N 0.9505481
## 1249 11250 N 0.9596814
## 491 10491 N 0.9879498
## 1690 11691 N 0.9929848
## 1470 11471 N 0.9975641
## sold.fctr.predict.Final.bayesglm
## 1390 Y
## 283 Y
## 1249 Y
## 491 Y
## 1690 Y
## 1470 Y
## sold.fctr.predict.Final.bayesglm.accurate
## 1390 FALSE
## 283 FALSE
## 1249 FALSE
## 491 FALSE
## 1690 FALSE
## 1470 FALSE
## sold.fctr.predict.Final.bayesglm.error
## 1390 0.3264140
## 283 0.3505481
## 1249 0.3596814
## 491 0.3879498
## 1690 0.3929848
## 1470 0.3975641
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.Final.bayesglm.prob"
## [2] "sold.fctr.predict.Final.bayesglm"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 186.502 192.37 5.868
## 16 predict.data.new 9 0 192.370 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.6if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 41## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 1860 11862 <NA> 0.1426249
## 1863 11865 <NA> 0.5852284
## 1889 11891 <NA> 0.9999594
## 2623 12625 <NA> 0.0168142
## sold.fctr.predict.Final.bayesglm
## 1860 N
## 1863 N
## 1889 Y
## 2623 N
## sold.fctr.predict.Final.bayesglm.accurate
## 1860 NA
## 1863 NA
## 1889 NA
## 2623 NA
## sold.fctr.predict.Final.bayesglm.error .label
## 1860 0 11862
## 1863 0 11865
## 1889 0 11891
## 2623 0 12625
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## NA NA <NA> NA
## NA.1 NA <NA> NA
## NA.2 NA <NA> NA
## NA.3 NA <NA> NA
## NA.4 NA <NA> NA
## NA.5 NA <NA> NA
## sold.fctr.predict.Final.bayesglm
## NA <NA>
## NA.1 <NA>
## NA.2 <NA>
## NA.3 <NA>
## NA.4 <NA>
## NA.5 <NA>
## sold.fctr.predict.Final.bayesglm.accurate
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## sold.fctr.predict.Final.bayesglm.error
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## NA.25 NA <NA> NA
## NA.81 NA <NA> NA
## NA.139 NA <NA> NA
## NA.148 NA <NA> NA
## NA.170 NA <NA> NA
## NA.737 NA <NA> NA
## sold.fctr.predict.Final.bayesglm
## NA.25 <NA>
## NA.81 <NA>
## NA.139 <NA>
## NA.148 <NA>
## NA.170 <NA>
## NA.737 <NA>
## sold.fctr.predict.Final.bayesglm.accurate
## NA.25 NA
## NA.81 NA
## NA.139 NA
## NA.148 NA
## NA.170 NA
## NA.737 NA
## sold.fctr.predict.Final.bayesglm.error
## NA.25 NA
## NA.81 NA
## NA.139 NA
## NA.148 NA
## NA.170 NA
## NA.737 NA
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## NA.792 NA <NA> NA
## NA.793 NA <NA> NA
## NA.794 NA <NA> NA
## NA.795 NA <NA> NA
## NA.796 NA <NA> NA
## NA.797 NA <NA> NA
## sold.fctr.predict.Final.bayesglm
## NA.792 <NA>
## NA.793 <NA>
## NA.794 <NA>
## NA.795 <NA>
## NA.796 <NA>
## NA.797 <NA>
## sold.fctr.predict.Final.bayesglm.accurate
## NA.792 NA
## NA.793 NA
## NA.794 NA
## NA.795 NA
## NA.796 NA
## NA.797 NA
## sold.fctr.predict.Final.bayesglm.error
## NA.792 NA
## NA.793 NA
## NA.794 NA
## NA.795 NA
## NA.796 NA
## NA.797 NA## Warning: Removed 798 rows containing missing values (geom_point).
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885)
for (obs_id in glb_force_prediction_lst[["0"]])
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
glb_force_prediction_lst[["1"]] <- c(11871, 11886, 11931, 11967, 11994, 11999,
12000, 12065, 12072, 12111, 12126,
12214, 12233, 12299, 12446, 12491,
12505, 12576, 12630)
for (obs_id in glb_force_prediction_lst[["1"]])
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by=".grpid", all.x=TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, submit_df, by=glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <- ((tmp_newobs_df$Probability1 >= 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 <= 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print("Prediction errors in duplicates:")## [1] "Prediction errors in duplicates:"print(tmp_newobs_df)## [1] UniqueID .grpid
## [3] sold.fctr.predict.Final.bayesglm sold.0
## [5] sold.1 sold.NA
## [7] .freq Probability1
## [9] .err
## <0 rows> (or 0-length row.names)if (nrow(tmp_newobs_df) > 0)
stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glb_txt_vars) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words TfIDf terms for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
TfIdf_mtrx <- glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(TfIdf_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glb_txt_vars)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(TfIdf_mtrx[, 246] > 0), c(glb_id_var, glb_txt_vars)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glb_txt_vars)]
#TfIdf_mtrx[2442, TfIdf_mtrx[2442, ] > 0]
print(sprintf(" Top_n post_stem_words TfIDf terms for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
top_n_vctr <- tmp_df$term[1:glb_top_n[[txt_var]]]
tmp_freq1_df <- subset(tmp_df, freq == 1)
tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words TfIDf terms for descr.my:"
## TfIdf term freq pos
## condit 207.6181 condit 495 133
## use 145.9567 use 290 611
## scratch 127.9087 scratch 286 499
## new 125.3927 new 156 385
## good 120.7748 good 197 260
## screen 106.8143 screen 213 500
## TfIdf term freq pos
## noth 13.436880 noth 14 391
## cabl 12.515281 cabl 18 103
## invis 4.689153 invis 5 304
## sprint 2.672499 sprint 2 549
## let 1.625083 let 1 328
## middl 1.263954 middl 1 365
## TfIdf term freq pos
## final 1.1375583 final 1 228
## fold 1.1375583 fold 1 236
## goe 1.1375583 goe 1 258
## high 1.1375583 high 1 280
## hole 1.1375583 hole 1 282
## 79in 0.9479652 79in 1 16
## UniqueID
## 520 10520
## descr.my
## 520 Apple iPad mini 1st Generation 16GB, Wi- Fi, 7.9in - Space Gray, great condition comes with the
## [1] 177
## [1] " Top_n post_stem_words TfIDf terms for descr.my:"
## TfIdf term freq pos top_n
## paperwork 1.421948 paperwork 1 418 TRUEif (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.6print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.X.bayesglm"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.bayesglm"print(dim(glb_fitobs_df))## [1] 974 69print(dsp_models_df)## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 10 All.X.bayesglm 0.8000000 0.8355584 0.5928708
## 12 All.X.no.rnorm.rpart 0.7966102 0.8103723 0.5861800
## 9 All.X.glm 0.7954802 0.8269987 0.5836701
## 8 Low.cor.X.glm 0.7864407 0.8296945 0.5677516
## 7 Interact.High.cor.Y.glm 0.7853107 0.8394608 0.5671369
## 11 All.X.glmnet 0.7853107 0.8374685 0.5653916
## 5 Max.cor.Y.rpart 0.7853107 0.8080205 0.5658292
## 13 All.X.no.rnorm.rf 0.7841808 0.8385905 0.5666714
## 6 Max.cor.Y.glm 0.7807910 0.8402773 0.5584673
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7728814 0.8075096 0.5406159
## 1 MFO.myMFO_classfr 0.5367232 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5367232 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.4632768 0.5191913 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 10 974.6030 0.6
## 12 NA 0.6
## 9 944.2624 0.6
## 8 935.6464 0.5
## 7 946.8663 0.4
## 11 NA 0.5
## 5 NA 0.5
## 13 NA 0.4
## 6 943.1953 0.4
## 4 NA 0.6
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
stop("not implemented yet")
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "All.X.bayesglm OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 423 52
## Y 125 285
## prdline.my .n.OOB .n.Tst .freqRatio.Tst .freqRatio.OOB
## 3 iPad 2 171 154 0.1929825 0.1932203
## 5 iPadAir 151 137 0.1716792 0.1706215
## 1 Unknown 97 87 0.1090226 0.1096045
## 2 iPad 1 99 89 0.1115288 0.1118644
## 4 iPad 3+ 136 123 0.1541353 0.1536723
## 6 iPadmini 127 114 0.1428571 0.1435028
## 7 iPadmini 2+ 104 94 0.1177945 0.1175141
## accurate.OOB.FALSE accurate.OOB.TRUE max.accuracy.OOB
## 3 36 135 0.7894737
## 5 30 121 0.8013245
## 1 28 69 0.7113402
## 2 22 77 0.7777778
## 4 21 115 0.8455882
## 6 21 106 0.8346457
## 7 19 85 0.8173077dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## N.x All.X.bayesglm.importance
## startprice.log 100.000000 100.000000
## biddable 94.720022 94.720022
## idseq.my 48.121762 48.121762
## prdline.my.fctr 32.302503 32.302503
## D.ratio.nstopwrds.nwrds 26.323096 26.323096
## D.TfIdf.sum.stem.stop.Ratio 23.352887 23.352887
## .rnorm 20.456519 20.456519
## D.npnct15.log 19.859878 19.859878
## D.npnct03.log 19.699198 19.699198
## D.terms.n.stem.stop.Ratio 19.469403 19.469403
## D.npnct01.log 18.926519 18.926519
## storage.fctr 18.264306 18.264306
## D.npnct28.log 18.184557 18.184557
## D.ratio.sum.TfIdf.nwrds 17.889190 17.889190
## D.npnct10.log 17.652897 17.652897
## D.npnct09.log 17.387066 17.387066
## D.npnct11.log 16.045510 16.045510
## D.npnct08.log 16.026016 16.026016
## D.TfIdf.sum.post.stop 15.682209 15.682209
## D.npnct12.log 15.644402 15.644402
## D.TfIdf.sum.post.stem 15.228525 15.228525
## D.sum.TfIdf 15.228525 15.228525
## D.npnct16.log 15.122193 15.122193
## D.npnct06.log 14.718131 14.718131
## D.ndgts.log 13.597509 13.597509
## D.npnct05.log 12.955382 12.955382
## D.npnct24.log 12.845505 12.845505
## D.npnct14.log 12.808880 12.808880
## D.npnct13.log 12.568451 12.568451
## D.nstopwrds.log 10.795659 10.795659
## .clusterid.fctr 10.171254 10.171254
## D.nwrds.log 9.988717 9.988717
## color.fctr 9.241439 9.241439
## D.nuppr.log 8.117272 8.117272
## D.nchrs.log 7.930601 7.930601
## carrier.fctr 6.640438 6.640438
## D.terms.n.post.stop 6.303720 6.303720
## D.nwrds.unq.log 6.158399 6.158399
## D.terms.n.post.stem 6.158399 6.158399
## cellular.fctr 4.979886 4.979886
## condition.fctr 0.000000 0.000000
## N.y importance
## startprice.log 100.000000 100.000000
## biddable 95.026117 95.026117
## idseq.my 49.511380 49.511380
## prdline.my.fctr 11.912115 11.912115
## D.ratio.nstopwrds.nwrds 27.012354 27.012354
## D.TfIdf.sum.stem.stop.Ratio 22.182931 22.182931
## .rnorm 18.288850 18.288850
## D.npnct15.log 20.409144 20.409144
## D.npnct03.log 19.628451 19.628451
## D.terms.n.stem.stop.Ratio 20.407526 20.407526
## D.npnct01.log 19.349887 19.349887
## storage.fctr 18.248570 18.248570
## D.npnct28.log 18.170112 18.170112
## D.ratio.sum.TfIdf.nwrds 16.015847 16.015847
## D.npnct10.log 18.632122 18.632122
## D.npnct09.log 17.752750 17.752750
## D.npnct11.log 17.304168 17.304168
## D.npnct08.log 16.596594 16.596594
## D.TfIdf.sum.post.stop 14.864382 14.864382
## D.npnct12.log 16.183600 16.183600
## D.TfIdf.sum.post.stem 14.423240 14.423240
## D.sum.TfIdf 14.423240 14.423240
## D.npnct16.log 16.010185 16.010185
## D.npnct06.log 15.782577 15.782577
## D.ndgts.log 14.605229 14.605229
## D.npnct05.log 13.947319 13.947319
## D.npnct24.log 12.688763 12.688763
## D.npnct14.log 15.280287 15.280287
## D.npnct13.log 13.585120 13.585120
## D.nstopwrds.log 12.403890 12.403890
## .clusterid.fctr 11.936703 11.936703
## D.nwrds.log 10.911417 10.911417
## color.fctr 13.332115 13.332115
## D.nuppr.log 9.940809 9.940809
## D.nchrs.log 9.277722 9.277722
## carrier.fctr 9.121777 9.121777
## D.terms.n.post.stop 8.265215 8.265215
## D.nwrds.unq.log 8.221215 8.221215
## D.terms.n.post.stem 8.221215 8.221215
## cellular.fctr 8.675136 8.675136
## condition.fctr 0.000000 0.000000
## Final.bayesglm.importance
## startprice.log 100.000000
## biddable 95.026117
## idseq.my 49.511380
## prdline.my.fctr 11.912115
## D.ratio.nstopwrds.nwrds 27.012354
## D.TfIdf.sum.stem.stop.Ratio 22.182931
## .rnorm 18.288850
## D.npnct15.log 20.409144
## D.npnct03.log 19.628451
## D.terms.n.stem.stop.Ratio 20.407526
## D.npnct01.log 19.349887
## storage.fctr 18.248570
## D.npnct28.log 18.170112
## D.ratio.sum.TfIdf.nwrds 16.015847
## D.npnct10.log 18.632122
## D.npnct09.log 17.752750
## D.npnct11.log 17.304168
## D.npnct08.log 16.596594
## D.TfIdf.sum.post.stop 14.864382
## D.npnct12.log 16.183600
## D.TfIdf.sum.post.stem 14.423240
## D.sum.TfIdf 14.423240
## D.npnct16.log 16.010185
## D.npnct06.log 15.782577
## D.ndgts.log 14.605229
## D.npnct05.log 13.947319
## D.npnct24.log 12.688763
## D.npnct14.log 15.280287
## D.npnct13.log 13.585120
## D.nstopwrds.log 12.403890
## .clusterid.fctr 11.936703
## D.nwrds.log 10.911417
## color.fctr 13.332115
## D.nuppr.log 9.940809
## D.nchrs.log 9.277722
## carrier.fctr 9.121777
## D.terms.n.post.stop 8.265215
## D.nwrds.unq.log 8.221215
## D.terms.n.post.stem 8.221215
## cellular.fctr 8.675136
## condition.fctr 0.000000print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))##
## N Y
## 602 196# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 192.370 198.286 5.917
## 17 display.session.info 10 0 198.287 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 20.128 76.969 56.841
## 11 fit.models 7 1 111.325 160.457 49.133
## 10 fit.models 7 0 86.042 111.324 25.282
## 12 fit.models 7 2 160.458 177.159 16.701
## 16 predict.data.new 9 0 192.370 198.286 5.917
## 15 fit.data.training 8 1 186.502 192.370 5.868
## 1 import.data 1 0 9.156 14.873 5.717
## 14 fit.data.training 8 0 181.481 186.502 5.021
## 13 fit.models 7 3 177.160 181.481 4.321
## 2 inspect.data 2 0 14.873 18.948 4.075
## 8 select.features 5 0 81.459 85.179 3.720
## 7 manage.missing.data 4 1 77.941 81.458 3.517
## 6 cluster.data 4 0 76.969 77.940 0.971
## 9 partition.data.training 6 0 85.179 86.041 0.863
## 3 scrub.data 2 1 18.948 19.654 0.706
## 4 transform.data 2 2 19.655 20.128 0.473
## duration
## 5 56.841
## 11 49.132
## 10 25.282
## 12 16.701
## 16 5.916
## 15 5.868
## 1 5.717
## 14 5.021
## 13 4.321
## 2 4.075
## 8 3.720
## 7 3.517
## 6 0.971
## 9 0.862
## 3 0.706
## 4 0.473
## [1] "Total Elapsed Time: 198.286 secs"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] C/en_US.UTF-8/C/C/C/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] randomForest_4.6-10 glmnet_2.0-2 arm_1.8-6
## [4] lme4_1.1-8 Matrix_1.2-2 MASS_7.3-43
## [7] rpart.plot_1.5.2 rpart_4.1-10 ROCR_1.0-7
## [10] gplots_2.17.0 sampling_2.7 tidyr_0.2.0
## [13] entropy_1.2.1 dynamicTreeCut_1.62 proxy_0.4-15
## [16] tm_0.6-2 NLP_0.1-8 stringr_1.0.0
## [19] dplyr_0.4.2 plyr_1.8.3 sqldf_0.4-10
## [22] RSQLite_1.0.0 DBI_0.3.1 gsubfn_0.6-6
## [25] proto_0.3-10 reshape2_1.4.1 gdata_2.17.0
## [28] doMC_1.3.3 iterators_1.0.7 foreach_1.4.2
## [31] doBy_4.5-13 survival_2.38-3 caret_6.0-52
## [34] ggplot2_1.0.1 lattice_0.20-33
##
## loaded via a namespace (and not attached):
## [1] splines_3.2.1 gtools_3.5.0 assertthat_0.1
## [4] stats4_3.2.1 yaml_2.1.13 slam_0.1-32
## [7] quantreg_5.11 pROC_1.8 chron_2.3-47
## [10] digest_0.6.8 RColorBrewer_1.1-2 minqa_1.2.4
## [13] colorspace_1.2-6 htmltools_0.2.6 lpSolve_5.6.11
## [16] BradleyTerry2_1.0-6 SparseM_1.6 scales_0.2.5
## [19] brglm_0.5-9 mgcv_1.8-6 car_2.0-25
## [22] nnet_7.3-10 lazyeval_0.1.10 pbkrtest_0.4-2
## [25] magrittr_1.5 evaluate_0.7 nlme_3.1-121
## [28] class_7.3-13 tools_3.2.1 formatR_1.2
## [31] munsell_0.4.2 compiler_3.2.1 e1071_1.6-6
## [34] caTools_1.17.1 nloptr_1.0.4 bitops_1.0-6
## [37] labeling_0.3 rmarkdown_0.7 gtable_0.1.2
## [40] codetools_0.2-14 abind_1.4-3 R6_2.1.0
## [43] knitr_1.10.5 KernSmooth_2.23-15 stringi_0.5-5
## [46] Rcpp_0.11.6 coda_0.17-1